Finding the needle in a high-dimensional haystack: Canonical correlation analysis for neuroscientists

The 21st century marks the emergence of "big data" with a rapid increase in the availability of datasets with multiple measurements. In neuroscience, brain-imaging datasets are more commonly accompanied by dozens or hundreds of phenotypic subject descriptors on the behavioral, neural, and genomic level. The complexity of such "big data" repositories offer new opportunities and pose new challenges for systems neuroscience. Canonical correlation analysis (CCA) is a prototypical family of methods that is useful in identifying the links between variable sets from different modalities. Importantly, CCA is well suited to describing relationships across multiple sets of data, such as in recently available big biomedical datasets. Our primer discusses the rationale, promises, and pitfalls of CCA.

Are innovation and new technologies in precision medicine paving a new era in patients centric care?

Healthcare is undergoing a transformation, and it is imperative to leverage new technologies to generate new data and support the advent of precision medicine (PM). Recent scientific breakthroughs and technological advancements have improved our understanding of disease pathogenesis and changed the way we diagnose and treat disease leading to more precise, predictable and powerful health care that is customized for the individual patient. Genetic, genomics, and epigenetic alterations appear to be contributing to different diseases. Deep clinical phenotyping, combined with advanced molecular phenotypic profiling, enables the construction of causal network models in which a genomic region is proposed to influence the levels of transcripts, proteins, and metabolites. Phenotypic analysis bears great importance to elucidat the pathophysiology of networks at the molecular and cellular level. Digital biomarkers (BMs) can have several applications beyond clinical trials in diagnostics-to identify patients affected by a disease or to guide treatment. Digital BMs present a big opportunity to measure clinical endpoints in a remote, objective and unbiased manner. However, the use of "omics" technologies and large sample sizes have generated massive amounts of data sets, and their analyses have become a major bottleneck requiring sophisticated computational and statistical methods. With the wealth of information for different diseases and its link to intrinsic biology, the challenge is now to turn the multi-parametric taxonomic classification of a disease into better clinical decision-making by more precisely defining a disease. As a result, the big data revolution has provided an opportunity to apply artificial intelligence (AI) and machine learning algorithms to this vast data set. The advancements in digital health opportunities have also arisen numerous questions and concerns on the future of healthcare practices in particular with what regards the reliability of AI diagnostic tools, the impact on clinical practice and vulnerability of algorithms. AI, machine learning algorithms, computational biology, and digital BMs will offer an opportunity to translate new data into actionable information thus, allowing earlier diagnosis and precise treatment options. A better understanding and cohesiveness of the different components of the knowledge network is a must to fully exploit the potential of it.

Precision medicine in the era of artificial intelligence: implications in chronic disease management

Aberrant metabolism is the root cause of several serious health issues, creating a huge burden to health and leading to diminished life expectancy. A dysregulated metabolism induces the secretion of several molecules which in turn trigger the inflammatory pathway. Inflammation is the natural reaction of the immune system to a variety of stimuli, such as pathogens, damaged cells, and harmful substances. Metabolically triggered inflammation, also called metaflammation or low-grade chronic inflammation, is the consequence of a synergic interaction between the host and the exposome-a combination of environmental drivers, including diet, lifestyle, pollutants and other factors throughout the life span of an individual. Various levels of chronic inflammation are associated with several lifestyle-related diseases such as diabetes, obesity, metabolic associated fatty liver disease (MAFLD), cancers, cardiovascular disorders (CVDs), autoimmune diseases, and chronic lung diseases. Chronic diseases are a growing concern worldwide, placing a heavy burden on individuals, families, governments, and health-care systems. New strategies are needed to empower communities worldwide to prevent and treat these diseases. Precision medicine provides a model for the next generation of lifestyle modification. This will capitalize on the dynamic interaction between an individual's biology, lifestyle, behavior, and environment. The aim of precision medicine is to design and improve diagnosis, therapeutics and prognostication through the use of large complex datasets that incorporate individual gene, function, and environmental variations. The implementation of high-performance computing (HPC) and artificial intelligence (AI) can predict risks with greater accuracy based on available multidimensional clinical and biological datasets. AI-powered precision medicine provides clinicians with an opportunity to specifically tailor early interventions to each individual. In this article, we discuss the strengths and limitations of existing and evolving recent, data-driven technologies, such as AI, in preventing, treating and reversing lifestyle-related diseases.

Multi-Omic Biological Age Estimation and Its Correlation With Wellness and Disease Phenotypes: A Longitudinal Study of 3,558 Individuals

Biological age (BA), derived from molecular and physiological measurements, has been proposed to better predict mortality and disease than chronological age (CA). In the present study, a computed estimate of BA was investigated longitudinally in 3,558 individuals using deep phenotyping, which encompassed a broad range of biological processes. The Klemera-Doubal algorithm was applied to longitudinal data consisting of genetic, clinical laboratory, metabolomic, and proteomic assays from individuals undergoing a wellness program. BA was elevated relative to CA in the presence of chronic diseases. We observed a significantly lower rate of change than the expected ~1 year/year (to which the estimation algorithm was constrained) in BA for individuals participating in a wellness program. This observation suggests that BA is modifiable and suggests that a lower BA relative to CA may be a sign of healthy aging. Measures of metabolic health, inflammation, and toxin bioaccumulation were strong predictors of BA. BA estimation from deep phenotyping was seen to change in the direction expected for both positive and negative health conditions. We believe BA represents a general and interpretable "metric for wellness" that may aid in monitoring aging over time.

A frame semantic overview of NLP-based information extraction for cancer-related EHR notes

OBJECTIVE

There is a lot of information about cancer in Electronic Health Record (EHR) notes that can be useful for biomedical research provided natural language processing (NLP) methods are available to extract and structure this information. In this paper, we present a scoping review of existing clinical NLP literature for cancer.

METHODS

We identified studies describing an NLP method to extract specific cancer-related information from EHR sources from PubMed, Google Scholar, ACL Anthology, and existing reviews. Two exclusion criteria were used in this study. We excluded articles where the extraction techniques used were too broad to be represented as frames (e.g., document classification) and also where very low-level extraction methods were used (e.g. simply identifying clinical concepts). 78 articles were included in the final review. We organized this information according to frame semantic principles to help identify common areas of overlap and potential gaps.

RESULTS

Frames were created from the reviewed articles pertaining to cancer information such as cancer diagnosis, tumor description, cancer procedure, breast cancer diagnosis, prostate cancer diagnosis and pain in prostate cancer patients. These frames included both a definition as well as specific frame elements (i.e. extractable attributes). We found that cancer diagnosis was the most common frame among the reviewed papers (36 out of 78), with recent work focusing on extracting information related to treatment and breast cancer diagnosis.

CONCLUSION

The list of common frames described in this paper identifies important cancer-related information extracted by existing NLP techniques and serves as a useful resource for future researchers requiring cancer information extracted from EHR notes. We also argue, due to the heavy duplication of cancer NLP systems, that a general purpose resource of annotated cancer frames and corresponding NLP tools would be valuable.

Study protocol for The Emory 3q29 Project: evaluation of neurodevelopmental, psychiatric, and medical symptoms in 3q29 deletion syndrome

BACKGROUND

3q29 deletion syndrome is caused by a recurrent hemizygous 1.6 Mb deletion on the long arm of chromosome 3. The syndrome is rare (1 in 30,000 individuals) and is associated with mild to moderate intellectual disability, increased risk for autism and anxiety, and a 40-fold increased risk for schizophrenia, along with a host of physical manifestations. However, the disorder is poorly characterized, the range of manifestations is not well described, and the underlying molecular mechanism is not understood. We designed the Emory 3q29 Project to document the range of neurodevelopmental and psychiatric manifestations associated with 3q29 deletion syndrome. We will also create a biobank of samples from our 3q29 deletion carriers for mechanistic studies, which will be a publicly-available resource for qualified investigators. The ultimate goals of our study are three-fold: first, to improve management and treatment of 3q29 deletion syndrome. Second, to uncover the molecular mechanism of the disorder. Third, to enable cross-disorder comparison with other rare genetic syndromes associated with neuropsychiatric phenotypes.

METHODS

We will ascertain study subjects, age 6 and older, from our existing registry ( 3q29deletion.org ). Participants and their families will travel to Atlanta, GA for phenotypic assessments, with particular emphasis on evaluation of anxiety, cognitive ability, autism symptomatology, and risk for psychosis via prodromal symptoms and syndromes. Evaluations will be performed using standardized instruments. Structural, diffusion, and resting-state functional MRI data will be collected from eligible study participants. We will also collect blood from the 3q29 deletion carrier and participating family members, to be banked at the NIMH Repository and Genomics Resource (NRGR).

DISCUSSION

The study of 3q29 deletion has the potential to transform our understanding of complex disease. Study of individuals with the deletion may provide insights into long term care and management of the disorder. Our project describes the protocol for a prospective study of the behavioral and clinical phenotype associated with 3q29 deletion syndrome. The paradigm described here could easily be adapted to study additional CNV or single gene disorders with high risk for neuropsychiatric phenotypes, and/or transferred to other study sites, providing a means for data harmonization and cross-disorder analysis.

Detecting time-evolving phenotypic topics via tensor factorization on electronic health records: Cardiovascular disease case study

OBJECTIVE

Discovering subphenotypes of complex diseases can help characterize disease cohorts for investigative studies aimed at developing better diagnoses and treatments. Recent advances in unsupervised machine learning on electronic health record (EHR) data have enabled researchers to discover phenotypes without input from domain experts. However, most existing studies have ignored time and modeled diseases as discrete events. Uncovering the evolution of phenotypes - how they emerge, evolve and contribute to health outcomes - is essential to define more precise phenotypes and refine the understanding of disease progression. Our objective was to assess the benefits of an unsupervised approach that incorporates time to model diseases as dynamic processes in phenotype discovery.

METHODS

In this study, we applied a constrained non-negative tensor-factorization approach to characterize the complexity of cardiovascular disease (CVD) patient cohort based on longitudinal EHR data. Through tensor-factorization, we identified a set of phenotypic topics (i.e., subphenotypes) that these patients established over the 10 years prior to the diagnosis of CVD, and showed the progress pattern. For each identified subphenotype, we examined its association with the risk for adverse cardiovascular outcomes estimated by the American College of Cardiology/American Heart Association Pooled Cohort Risk Equations, a conventional CVD-risk assessment tool frequently used in clinical practice. Furthermore, we compared the subsequent myocardial infarction (MI) rates among the six most prevalent subphenotypes using survival analysis.

RESULTS

From a cohort of 12,380 adult CVD individuals with 1068 unique PheCodes, we successfully identified 14 subphenotypes. Through the association analysis with estimated CVD risk for each subtype, we found some phenotypic topics such as Vitamin D deficiency and depression, Urinary infections cannot be explained by the conventional risk factors. Through a survival analysis, we found markedly different risks of subsequent MI following the diagnosis of CVD among the six most prevalent topics (p < 0.0001), indicating these topics may capture clinically meaningful subphenotypes of CVD.

CONCLUSION

This study demonstrates the potential benefits of using tensor-decomposition to model diseases as dynamic processes from longitudinal EHR data. Our results suggest that this data-driven approach may potentially help researchers identify complex and chronic disease subphenotypes in precision medicine research.

An information model for computable cancer phenotypes

BACKGROUND

Standards, methods, and tools supporting the integration of clinical data and genomic information are an area of significant need and rapid growth in biomedical informatics. Integration of cancer clinical data and cancer genomic information poses unique challenges, because of the high volume and complexity of clinical data, as well as the heterogeneity and instability of cancer genome data when compared with germline data. Current information models of clinical and genomic data are not sufficiently expressive to represent individual observations and to aggregate those observations into longitudinal summaries over the course of cancer care. These models are acutely needed to support the development of systems and tools for generating the so called clinical "deep phenotype" of individual cancer patients, a process which remains almost entirely manual in cancer research and precision medicine.

METHODS

Reviews of existing ontologies and interviews with cancer researchers were used to inform iterative development of a cancer phenotype information model. We translated a subset of the Fast Healthcare Interoperability Resources (FHIR) models into the OWL 2 Description Logic (DL) representation, and added extensions as needed for modeling cancer phenotypes with terms derived from the NCI Thesaurus. Models were validated with domain experts and evaluated against competency questions.

RESULTS

The DeepPhe Information model represents cancer phenotype data at increasing levels of abstraction from mention level in clinical documents to summaries of key events and findings. We describe the model using breast cancer as an example, depicting methods to represent phenotypic features of cancers, tumors, treatment regimens, and specific biologic behaviors that span the entire course of a patient's disease.

CONCLUSIONS

We present a multi-scale information model for representing individual document mentions, document level classifications, episodes along a disease course, and phenotype summarization, linking individual observations to high-level summaries in support of subsequent integration and analysis.

Longitudinal determination of resilience in humans to identify mechanisms of resilience to modern-life stressors: the longitudinal resilience assessment (LORA) study

Resilience is the maintenance and/or quick recovery of mental health during and after periods of adversity. It is conceptualized to result from a dynamic process of successful adaptation to stressors. Up to now, a large number of resilience factors have been proposed, but the mechanisms underlying resilience are not yet understood. To shed light on the complex and time-varying processes of resilience that lead to a positive long-term outcome in the face of adversity, the Longitudinal Resilience Assessment (LORA) study has been established. In this study, 1191 healthy participants are followed up at 3- and 18-month intervals over a course of 4.5 years at two study centers in Germany. Baseline and 18-month visits entail multimodal phenotyping, including the assessment of mental health status, sociodemographic and lifestyle variables, resilience factors, life history, neuropsychological assessments (of proposed resilience mechanisms), and biomaterials (blood for genetic and epigenetic, stool for microbiome, and hair for cortisol analysis). At 3-monthly online assessments, subjects are monitored for subsequent exposure to stressors as well as mental health measures, which allows for a quantitative assessment of stressor-dependent changes in mental health as the main outcome. Descriptive analyses of mental health, number of stressors including major life events, daily hassles, perceived stress, and the ability to recover from stress are here presented for the baseline sample. The LORA study is unique in its design and will pave the way for a better understanding of resilience mechanisms in humans and for further development of interventions to successfully prevent stress-related disorder.

Deep phenotyping of patients with Tuberous Sclerosis Complex and no mutation identified in TSC1 and TSC2

Tuberous Sclerosis Complex (TSC) is a multisystemic condition caused by mutations in TSC1 or TSC2, but a pathogenic variant is not identified in up to 10% of the patients. The aim of this study was to delineate the phenotype of pediatric and adult patients with a definite clinical diagnosis of TSC and no mutation identified in TSC1 or TSC2. We collected molecular and clinical data of 240 patients with TSC, assessing over 50 variables. We compared the phenotype of the homogeneous group of individuals with No Mutation Identified (NMI) with that of TSC patients with a TSC1 and TSC2 pathogenic variant. 9.17% of individuals were classified as NMI. They were diagnosed at an older age (p = 0.001), had more frequent normal cognition (p < 0.001) and less frequent epilepsy (p = 0.010), subependymal nodules (p = 0.022) and giant cell astrocytomas (p = 0.008) than patients with TSC2 pathogenic variants. NMI individuals showed more frequent bilateral and larger renal angiomyolipomas (p = 0.001; p = 0.003) and pulmonary involvement (trend) than patients with TSC1 pathogenic variants. Only one NMI individual had intellectual disability. None presented with a subependymal giant cell astrocytoma. Other medical problems not typical of TSC were found in 42.86%, without a recurrent pattern of abnormalities. Other TSC-associated neuropsychiatric disorders and drug-resistance in epilepsy were equally frequent in the three groups. This study provides a systematic clinical characterization of patients with TSC and facilitates the delineation of a distinctive phenotype indicative of NMI patients, with important implications for surveillance.

Amblyopia and the whole child

Amblyopia is a disorder of neurodevelopment that occurs when there is discordant binocular visual experience during the first years of life. While treatments are effective in improving visual acuity, there are significant individual differences in response to treatment that cannot be attributed solely to difference in adherence. In this considerable variability in response to treatment, we argue that treatment outcomes might be optimized by utilizing deep phenotyping of amblyopic deficits to guide alternative treatment choices. In addition, an understanding of the broader knock-on effects of amblyopia on developing visually-guided skills, self-perception, and quality of life will facilitate a whole person healthcare approach to amblyopia.

Multiplatform metabolomics for an integrative exploration of metabolic syndrome in older men

BACKGROUND

Metabolic syndrome (MetS), a cluster of factors associated with risks of developing cardiovascular diseases, is a public health concern because of its growing prevalence. Considering the combination of concomitant components, their development and severity, MetS phenotypes are largely heterogeneous, inducing disparity in diagnosis.

METHODS

A case/control study was designed within the NuAge longitudinal cohort on aging. From a 3-year follow-up of 123 stable individuals, we present a deep phenotyping approach based on a multiplatform metabolomics and lipidomics untargeted strategy to better characterize metabolic perturbations in MetS and define a comprehensive MetS signature stable over time in older men.

FINDINGS

We characterize significant changes associated with MetS, involving modulations of 476 metabolites and lipids, and representing 16% of the detected serum metabolome/lipidome. These results revealed a systemic alteration of metabolism, involving various metabolic pathways (urea cycle, amino-acid, sphingo- and glycerophospholipid, and sugar metabolisms…) not only intrinsically interrelated, but also reflecting environmental factors (nutrition, microbiota, physical activity…).

INTERPRETATION

These findings allowed identifying a comprehensive MetS signature, reduced to 26 metabolites for future translation into clinical applications for better diagnosing MetS.

FUNDING

The NuAge Study was supported by a research grant from the Canadian Institutes of Health Research (CIHR; MOP-62842). The actual NuAge Database and Biobank, containing data and biologic samples of 1,753 NuAge participants (from the initial 1,793 participants), are supported by the Fonds de recherche du Québec (FRQ; 2020-VICO-279753), the Quebec Network for Research on Aging, a thematic network funded by the Fonds de Recherche du Québec - Santé (FRQS) and by the Merck-Frost Chair funded by La Fondation de l'Université de Sherbrooke. All metabolomics and lipidomics analyses were funded and performed within the metaboHUB French infrastructure (ANR-INBS-0010). All authors had full access to the full data in the study and accept responsibility to submit for publication.

HPO2Vec+: Leveraging heterogeneous knowledge resources to enrich node embeddings for the Human Phenotype Ontology

BACKGROUND

In precision medicine, deep phenotyping is defined as the precise and comprehensive analysis of phenotypic abnormalities, aiming to acquire a better understanding of the natural history of a disease and its genotype-phenotype associations. Detecting phenotypic relevance is an important task when translating precision medicine into clinical practice, especially for patient stratification tasks based on deep phenotyping. In our previous work, we developed node embeddings for the Human Phenotype Ontology (HPO) to assist in phenotypic relevance measurement incorporating distributed semantic representations. However, the derived HPO embeddings hold only distributed representations for IS-A relationships among nodes, hampering the ability to fully explore the graph.

METHODS

In this study, we developed a framework, HPO2Vec+, to enrich the produced HPO embeddings with heterogeneous knowledge resources (i.e., DECIPHER, OMIM, and Orphanet) for detecting phenotypic relevance. Specifically, we parsed disease-phenotype associations contained in these three resources to enrich non-inheritance relationships among phenotypic nodes in the HPO. To generate node embeddings for the HPO, node2vec was applied to perform node sampling on the enriched HPO graphs based on random walk followed by feature learning over the sampled nodes to generate enriched node embeddings. Four HPO embeddings were generated based on different graph structures, which we hereafter label as HPOEmb-Original, HPOEmb-DECIPHER, HPOEmb-OMIM, and HPOEmb-Orphanet. We evaluated the derived embeddings quantitatively through an HPO link prediction task with four edge embeddings operations and six machine learning algorithms. The resulting best embeddings were then evaluated for patient stratification of 10 rare diseases using electronic health records (EHR) collected at Mayo Clinic. We assessed our framework qualitatively by visualizing phenotypic clusters and conducting a use case study on primary hyperoxaluria (PH), a rare disease, on the task of inferring relevant phenotypes given 22 annotated PH related phenotypes.

RESULTS

The quantitative link prediction task shows that HPOEmb-Orphanet achieved an optimal AUROC of 0.92 and an average precision of 0.94. In addition, HPOEmb-Orphanet achieved an optimal F1 score of 0.86. The quantitative patient similarity measurement task indicates that HPOEmb-Orphanet achieved the highest average detection rate for similar patients over 10 rare diseases and performed better than other similarity measures implemented by an existing tool, HPOSim, especially for pairwise patients with fewer shared common phenotypes. The qualitative evaluation shows that the enriched HPO embeddings are generally able to detect relationships among nodes with fine granularity and HPOEmb-Orphanet is particularly good at associating phenotypes across different disease systems. For the use case of detecting relevant phenotypic characterizations for given PH related phenotypes, HPOEmb-Orphanet outperformed the other three HPO embeddings by achieving the highest average P@5 of 0.81 and the highest P@10 of 0.79. Compared to seven conventional similarity measurements provided by HPOSim, HPOEmb-Orphanet is able to detect more relevant phenotypic pairs, especially for pairs not in inheritance relationships.

CONCLUSION

We drew the following conclusions based on the evaluation results. First, with additional non-inheritance edges, enriched HPO embeddings can detect more associations between fine granularity phenotypic nodes regardless of their topological structures in the HPO graph. Second, HPOEmb-Orphanet not only can achieve the optimal performance through link prediction and patient stratification based on phenotypic similarity, but is also able to detect relevant phenotypes closer to domain expert's judgments than other embeddings and conventional similarity measurements. Third, incorporating heterogeneous knowledge resources do not necessarily result in better performance for detecting relevant phenotypes. From a clinical perspective, in our use case study, clinical-oriented knowledge resources (e.g., Orphanet) can achieve better performance in detecting relevant phenotypic characterizations compared to biomedical-oriented knowledge resources (e.g., DECIPHER and OMIM).

The importance of an integrated genotype-phenotype strategy to unravel the molecular bases of titinopathies

Next generation sequencing (NGS) has allowed the titin gene (TTN) to be identified as a major contributor to neuromuscular disorders, with high clinical heterogeneity. The mechanisms underlying the phenotypic variability and the dominant or recessive pattern of inheritance are unclear. Titin is involved in the formation and stability of the sarcomeres. The effects of the different TTN variants can be harmless or pathogenic (recessive or dominant) but the interpretation is tricky because the current bioinformatics tools can not predict their functional impact effectively. Moreover, TTN variants are very frequent in the general population. The combination of deep phenotyping associated with RNA molecular analyses, western blot (WB) and functional studies is often essential for the interpretation of genetic variants in patients suspected of titinopathy. In line with the current guidelines and suggestions, we implemented for patients with skeletal myopathy and with potentially disease causing TTN variant(s) an integrated genotype-transcripts-protein-phenotype approach, associated with phenotype and variants segregation studies in relatives and confrontation with published data on titinopathies to evaluate pathogenic effects of TTN variants (even truncating ones) on titin transcripts, amount, size and functionality. We illustrate this integrated approach in four patients with recessive congenital myopathy.

The future of deep phenotyping in osteoarthritis: How can high throughput omics technologies advance our understanding of the cellular and molecular taxonomy of the disease?

Osteoarthritis (OA) is the most common form of musculoskeletal disease with significant healthcare costs and unmet needs in terms of early diagnosis and treatment. Many of the drugs that have been developed to treat OA failed in phase 2 and phase 3 clinical trials or produced inconclusive and ambiguous results. High throughput omics technologies are a powerful tool to better understand the mechanisms of the development of OA and other arthritic diseases. In this paper we outline the strategic reasons for increasingly applying deep phenotyping in OA for the benefit of gaining a better understanding of disease mechanisms and developing targeted treatments. This editorial is intended to launch a special themed issue of Osteoarthritis and Cartilage Open addressing the timely topic of "Advances in omics technologies for deep phenotyping in osteoarthritis". High throughput omics technologies are increasingly being applied in mechanistic studies of OA and other arthritic diseases. Applying multi-omics approaches in OA is a high priority and will allow us to gather new information on disease pathogenesis at the cellular level, and integrate data from diverse omics technology platforms to enable deep phenotyping. We anticipate that new knowledge in this area will allow us to harness the power of Big Data Analytics and resolve the extremely complex and overlapping clinical phenotypes into molecular endotypes, revealing new information about the cellular taxonomy of OA and "druggable pathways", thus facilitating future drug development.

A deep phenotyping experience: up to date in management and diagnosis of Malan syndrome in a single center surveillance report

Background

Malan syndrome (MALNS) is a recently described ultrarare syndrome lacking guidelines for diagnosis, management and monitoring of evolutive complications. Less than 90 patients are reported in the literature and limited clinical information are available to assure a proper health surveillance.

Results

A multidisciplinary team with high expertise in MALNS has been launched at the “Ospedale Pediatrico Bambino Gesù”, Rome, Italy. Sixteen Italian MALNS individuals with molecular confirmed clinical diagnosis of MALNS were enrolled in the program. For all patients, 1-year surveillance in a dedicated outpatient Clinic was attained. The expert panel group enrolled 16 patients and performed a deep phenotyping analysis directed to clinically profiling the disorder and performing critical revision of previously reported individuals. Some evolutive complications were also assessed. Previously unappreciated features (e.g., high risk of bone fractures in childhood, neurological/neurovegetative symptoms, noise sensitivity and Chiari malformation type 1) requiring active surveillance were identified. A second case of neoplasm was recorded. No major cardiovascular anomalies were noticed. An accurate clinical description of 9 new MALNS cases was provided.

Conclusions

Deep phenotyping has provided a more accurate characterization of the main clinical features of MALNS and allows broadening the spectrum of disease. A minimal dataset of clinical evaluations and follow-up timeline has been proposed for proper management of patients affected by this ultrarare disorder.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13023-022-02384-9.

Genes Involved in Neurodevelopment, Neuroplasticity and Major Depression: No Association for CACNA1C, CHRNA7 and MAPK1

Objective

Genetics factors are likely to play a role in the risk, clinical presentation and treatment outcome in major depressive disorder (MDD). In this study, we investigated the role of three candidate genes for MDD; calcium voltage-gated channel subunit alpha1 C (CACNA1C ), cholinergic receptor nicotinic alpha 7 subunit (CHRNA7 ), and mitogen-activated protein kinase 1 (MAPK1).

Methods

Two-hundred forty-two MDD patients and 326 healthy controls of Korean ancestry served as samples for the analyses. Thirty-nine single nucleotide polymorphisms (SNPs) within CACNA1C, CHRNA7, and MAPK1 genes were genotyped and subsequently tested for association with MDD (primary analysis) and other clinical features (symptoms’ severity, age of onset, history of suicide attempt, treatment outcome) (secondary analyses). Single SNPs, haplotypes and epistatic analyses were performed.

Results

Single SNPs were not associated with disease risk and clinical features. However, a combination of alleles (haplotype) within MAPK1 was found associated with MDD-status. Secondary analyses detected a possible involvement of CACNA1C haplotype in resistance to antidepressant treatment.

Conclusion

These data suggest a role for MAPK1 and CACNA1C in MDD risk and treatment resistance, respectively. However, since many limitations characterize the analysis, the results must be considered with great caution and verified.

MixEHR-Guided: A guided multi-modal topic modeling approach for large-scale automatic phenotyping using the electronic health record

Electronic Health Records (EHRs) contain rich clinical data collected at the point of the care, and their increasing adoption offers exciting opportunities for clinical informatics, disease risk prediction, and personalized treatment recommendation. However, effective use of EHR data for research and clinical decision support is often hampered by a lack of reliable disease labels. To compile gold-standard labels, researchers often rely on clinical experts to develop rule-based phenotyping algorithms from billing codes and other surrogate features. This process is tedious and error-prone due to recall and observer biases in how codes and measures are selected, and some phenotypes are incompletely captured by a handful of surrogate features. To address this challenge, we present a novel automatic phenotyping model called MixEHR-Guided (MixEHR-G), a multimodal hierarchical Bayesian topic model that efficiently models the EHR generative process by identifying latent phenotype structure in the data. Unlike existing topic modeling algorithms wherein the inferred topics are not identifiable, MixEHR-G uses prior information from informative surrogate features to align topics with known phenotypes. We applied MixEHR-G to an openly-available EHR dataset of 38,597 intensive care patients (MIMIC-III) in Boston, USA and to administrative claims data for a population-based cohort (PopHR) of 1.3 million people in Quebec, Canada. Qualitatively, we demonstrate that MixEHR-G learns interpretable phenotypes and yields meaningful insights about phenotype similarities, comorbidities, and epidemiological associations. Quantitatively, MixEHR-G outperforms existing unsupervised phenotyping methods on a phenotype label annotation task, and it can accurately estimate relative phenotype prevalence functions without gold-standard phenotype information. Altogether, MixEHR-G is an important step towards building an interpretable and automated phenotyping system using EHR data.

Deep Clinical Phenotyping of Parkinson's Disease: Towards a New Era of Research and Clinical Care

Despite recent advances in technology, clinical phenotyping of Parkinson's disease (PD) has remained relatively limited as current assessments are mainly based on empirical observation and subjective categorical judgment at the clinic. A lack of comprehensive, objective, and quantifiable clinical phenotyping data has hindered our capacity to diagnose, assess patients' conditions, discover pathogenesis, identify preclinical stages and clinical subtypes, and evaluate new therapies. Therefore, deep clinical phenotyping of PD patients is a necessary step towards understanding PD pathology and improving clinical care. In this review, we present a growing community consensus and perspective on how to clinically phenotype this disease, that is, to phenotype the entire course of disease progression by integrating capacity, performance, and perception approaches with state-of-the-art technology. We also explore the most studied aspects of PD deep clinical phenotypes, namely, bradykinesia, tremor, dyskinesia and motor fluctuation, gait impairment, speech impairment, and non-motor phenotypes.

Pathway analysis of genomic pathology tests for prognostic cancer subtyping

Genomic test results collected during the provision of medical care and stored in Electronic Health Record (EHR) systems represent an opportunity for clinical research into disease heterogeneity and clinical outcomes. In this paper, we evaluate the use of genomic test reports ordered for cancer patients in order to derive cancer subtypes and to identify biological pathways predictive of poor survival outcomes. A novel method is proposed to calculate patient similarity based on affected biological pathways rather than gene mutations. We demonstrate that this approach identifies subtypes of prognostic value and biological pathways linked to survival, with implications for precision treatment selection and a better understanding of the underlying disease. We also share lessons learned regarding the opportunities and challenges of secondary use of observational genomic data to conduct such research.

Genetic Variants Within Molecular Targets of Antipsychotic Treatment: Effects on Treatment Response, Schizophrenia Risk, and Psychopathological Features

Schizophrenia (SCZ) is a common and severe mental disorder. Genetic factors likely play a role in its pathophysiology as well as in treatment response. In the present study, we investigated the effects of several single nucleotide polymorphisms (SNPs) within 9 genes involved with antipsychotic (AP) mechanisms of action. Two independent samples were recruited. The Korean sample included 176 subjects diagnosed with SCZ and 326 healthy controls, while the Italian sample included 83 subjects and 194 controls. AP response as measured by the positive and negative syndrome scale (PANSS) was the primary outcome, while the secondary outcome was the SCZ risk. Exploratory analyses were performed on (1) symptom clusters response (as measured by PANSS subscales); (2) age of onset; (3) family history; and (4) suicide history. Associations evidenced in the primary analyses did not survive to the FDR correction. Concerning SCZ risk, we partially confirmed the associations among COMT and MAPK1 genetic variants and SCZ. Finally, our exploratory analysis suggested that CHRNA7 and HTR2A genes may modulate both positive and negative symptoms responses, while PLA2G4A and SIGMAR1 may modulate respectively positive and negative symptoms responses. Moreover, GSK3B, HTR2A, PLA2G4A, and S100B variants may determine an anticipation of SCZ age of onset. Our results did not support a primary role for the genes investigated in AP response as a whole. However, our exploratory findings suggested that these genes may be involved in symptom clusters response.

Novel Analysis Methods to Mine Immune-Mediated Phenotypes and Find Genetic Variation Within the Electronic Health Record (Roadmap for Phenotype to Genotype: Immunogenomics)

The field of immunogenomics has the opportunity for accelerated genetic discovery aided by the maturation of electronic health records (EHRs) linked to DNA biobanks. Novel analysis methods in deep phenotyping of EHR data allow the full realization of the paired and increasingly dense genetic/phenotypic information available. This enables researchers to uncover genetic risk factors for the prevention and optimal treatment of immune-mediated diseases and immune-mediated adverse drug reactions. This article reviews the background of EHRs linked to DNA biobanks, potential applications to immunogenomic discovery, and current and emerging techniques in EHR-based deep phenotyping.

A behavioural exploration of language aptitude and experience, cognition and more using Graph Analysis

Language aptitude has recently regained interest in cognitive neuroscience. Traditional language aptitude testing included phonemic coding ability, associative memory, grammatical sensitivity and inductive language learning. Moreover, domain-general cognitive abilities are associated with individual differences in language aptitude, together with factors that have yet to be elucidated. Beyond domain-general cognition, it is also likely that aptitude and experience in domain-specific but non-linguistic fields (e.g. music or numerical processing) influence and are influenced by language aptitude. We investigated some of these relationships in a sample of 152 participants, using exploratory graph analysis, across different levels of regularisation, i.e. sensitivity. We carried out a meta cluster analysis in a second step to identify variables that are robustly grouped together. We discuss the data, as well as their meta-network groupings, at a baseline network sensitivity level, and in two analyses, one including and the other excluding dyslexic readers. Our results show a stable association between language and cognition, and the isolation of multilingual language experience, musicality and literacy. We highlight the necessity of a more comprehensive view of language and of cognition as multivariate systems.

Remote myocardial fibrosis predicts adverse outcome in patients with myocardial infarction on clinical cardiovascular magnetic resonance imaging

BACKGROUND

Heart failure (HF) most commonly occurs in patients who have had a myocardial infarction (MI), but factors other than MI size may be deterministic. Fibrosis of myocardium remote from the MI is associated with adverse remodeling. We aimed to 1) investigate the association between remote myocardial fibrosis, measured using cardiovascular magnetic resonance (CMR) extracellular volume fraction (ECV), and HF and death following MI, 2) identify predictors of remote myocardial fibrosis in patients with evidence of MI and determine the relationship with infarct size.

METHODS

Multicenter prospective cohort study of 1199 consecutive patients undergoing CMR with evidence of MI on late gadolinium enhancement. Median follow-up was 1133 (895-1442) days. Cox proportional hazards modeling was used to identify factors predictive of the primary outcome, a composite of first hospitalization for HF (HHF) or all-cause mortality, post-CMR. Linear regression modeling was used to identify determinants of remote ECV.

RESULTS

Remote myocardial fibrosis was a strong predictor of primary outcome (χ2: 15.6, hazard ratio [HR]: 1.07 per 1% increase in ECV, 95% confidence interval [CI]: 1.04-1.11, p < 0.001) and was separately predictive of both HHF and death. The strongest predictors of remote ECV were diabetes, sex, natriuretic peptides, and body mass index, but, despite extensive phenotyping, the adjusted model R2 was only 0.283. The relationship between infarct size and remote fibrosis was very weak.

CONCLUSION

Myocardial fibrosis, measured using CMR ECV, is a strong predictor of HHF and death in patients with evidence of MI. The mechanisms underlying remote myocardial fibrosis formation post-MI remain poorly understood, but factors other than infarct size appear to be important.

Association of deep phenotyping with diagnostic yield of prenatal exome sequencing for fetal brain abnormalities

PURPOSE

To evaluate whether deep prenatal phenotyping of fetal brain abnormalities (FBAs) increases diagnostic yield of trio-exome sequencing (ES) compared with standard phenotyping.

METHODS

Retrospective exploratory analysis of a multicenter prenatal ES study. Participants were eligible if an FBA was diagnosed and subsequently found to have a normal microarray. Deep phenotyping was defined as phenotype based on targeted ultrasound plus prenatal/postnatal magnetic resonance imaging, autopsy, and/or known phenotypes of other affected family members. Standard phenotyping was based on targeted ultrasound alone. FBAs were categorized by major brain findings on prenatal ultrasound. Cases with positive ES results were compared with those that have negative results by available phenotyping, as well as diagnosed FBAs.

RESULTS

A total of 76 trios with FBAs were identified, of which 25 (33%) cases had positive ES results and 51 (67%) had negative results. Individual modalities of deep phenotyping were not associated with diagnostic ES results. The most common FBAs identified were posterior fossa anomalies and midline defects. Neural tube defects were significantly associated with receipt of a negative ES result (0% vs 22%, P = .01).

CONCLUSION

Deep phenotyping was not associated with increased diagnostic yield of ES for FBA in this small cohort. Neural tube defects were associated with negative ES results.