Disentangling genetic and environmental risk factors for individual diseases from multiplex comorbidity networks

Schizophrenia and Rheumatoid Arthritis Genetic Scenery: Potential Non-HLA Genes Involved in Both Diseases Relationship

Background: The link between rheumatoid arthritis (RA) and schizophrenia (SZ) has long been a hot topic of deliberation among scientists from various fields. Especially when it comes to genetics, the connection between RA and SZ is still up for discussion, as can be observed in this study. The HLA genes are the most disputed in identifying a connection between the two diseases, but a more thorough investigation of other genes that may be ignored could yield something even more interesting. Thus, finding the genes responsible for this long-sought relationship will necessitate looking for them. Materials and Methods: Shared and overlapped associated genes involved between SZ and RA were extracted from four databases. The overlapping genes were examined using Database for Annotation, Visualization and Integrated Discovery (DAVID) and InnateDB to search the pertinent genes that concatenate between these two disorders. Results: A total of 91 overlapped genes were discovered, and that 13 genes, divided into two clusters, showed a similarity in function, suggesting that they may serve as an important meeting point. FCGR2A, IL18R, BTNL2, AGER, and CTLA4 are five non-HLA genes related to the immune system, which could lead to new discoveries about the connection between these two disorders. Conclusion: An in-depth investigation of these functionally comparable non-HLA genes that overlap could reveal new interesting information in both diseases. Understanding the molecular and immune-related aspects of RA and SZ may shed light on their etiology and inform future research on targeted treatment strategies.

Molecular bases of comorbidities: present and future perspectives

Co-occurrence of diseases decreases patient quality of life, complicates treatment choices, and increases mortality. Analyses of electronic health records present a complex scenario of comorbidity relationships that vary by age, sex, and cohort under study. The study of similarities between diseases using 'omics data, such as genes altered in diseases, gene expression, proteome, and microbiome, are fundamental to uncovering the origin of, and potential treatment for, comorbidities. Recent studies have produced a first generation of genetic interpretations for as much as 46% of the comorbidities described in large cohorts. Integrating different sources of molecular information and using artificial intelligence (AI) methods are promising approaches for the study of comorbidities. They may help to improve the treatment of comorbidities, including the potential repositioning of drugs.

A network medicine approach to study comorbidities in heart failure with preserved ejection fraction

BACKGROUND

Comorbidities are expected to impact the pathophysiology of heart failure (HF) with preserved ejection fraction (HFpEF). However, comorbidity profiles are usually reduced to a few comorbid disorders. Systems medicine approaches can model phenome-wide comorbidity profiles to improve our understanding of HFpEF and infer associated genetic profiles.

METHODS

We retrospectively explored 569 comorbidities in 29,047 HF patients, including 8062 HFpEF and 6585 HF with reduced ejection fraction (HFrEF) patients from a German university hospital. We assessed differences in comorbidity profiles between HF subtypes via multiple correspondence analysis. Then, we used machine learning classifiers to identify distinctive comorbidity profiles of HFpEF and HFrEF patients. Moreover, we built a comorbidity network (HFnet) to identify the main disease clusters that summarized the phenome-wide comorbidity. Lastly, we predicted novel gene candidates for HFpEF by linking the HFnet to a multilayer gene network, integrating multiple databases. To corroborate HFpEF candidate genes, we collected transcriptomic data in a murine HFpEF model. We compared predicted genes with the murine disease signature as well as with the literature.

RESULTS

We found a high degree of variance between the comorbidity profiles of HFpEF and HFrEF, while each was more similar to HFmrEF. The comorbidities present in HFpEF patients were more diverse than those in HFrEF and included neoplastic, osteologic and rheumatoid disorders. Disease communities in the HFnet captured important comorbidity concepts of HF patients which could be assigned to HF subtypes, age groups, and sex. Based on the HFpEF comorbidity profile, we predicted and recovered gene candidates, including genes involved in fibrosis (COL3A1, LOX, SMAD9, PTHL), hypertrophy (GATA5, MYH7), oxidative stress (NOS1, GSST1, XDH), and endoplasmic reticulum stress (ATF6). Finally, predicted genes were significantly overrepresented in the murine transcriptomic disease signature providing additional plausibility for their relevance.

CONCLUSIONS

We applied systems medicine concepts to analyze comorbidity profiles in a HF patient cohort. We were able to identify disease clusters that helped to characterize HF patients. We derived a distinct comorbidity profile for HFpEF, which was leveraged to suggest novel candidate genes via network propagation. The identification of distinctive comorbidity profiles and candidate genes from routine clinical data provides insights that may be leveraged to improve diagnosis and identify treatment targets for HFpEF patients.

Visualizing novel connections and genetic similarities across diseases using a network-medicine based approach

Understanding the genetic relationships between human disorders could lead to better treatment and prevention strategies, especially for individuals with multiple comorbidities. A common resource for studying genetic-disease relationships is the GWAS Catalog, a large and well curated repository of SNP-trait associations from various studies and populations. Some of these populations are contained within mega-biobanks such as the Million Veteran Program (MVP), which has enabled the genetic classification of several diseases in a large well-characterized and heterogeneous population. Here we aim to provide a network of the genetic relationships among diseases and to demonstrate the utility of quantifying the extent to which a given resource such as MVP has contributed to the discovery of such relations. We use a network-based approach to evaluate shared variants among thousands of traits in the GWAS Catalog repository. Our results indicate many more novel disease relationships that did not exist in early studies and demonstrate that the network can reveal clusters of diseases mechanistically related. Finally, we show novel disease connections that emerge when MVP data is included, highlighting methodology that can be used to indicate the contributions of a given biobank.

Network-medicine framework for studying disease trajectories in U.S. veterans

A better understanding of the sequential and temporal aspects in which diseases occur in patient's lives is essential for developing improved intervention strategies that reduce burden and increase the quality of health services. Here we present a network-based framework to study disease relationships using Electronic Health Records from > 9 million patients in the United States Veterans Health Administration (VHA) system. We create the Temporal Disease Network, which maps the sequential aspects of disease co-occurrence among patients and demonstrate that network properties reflect clinical aspects of the respective diseases. We use the Temporal Disease Network to identify disease groups that reflect patterns of disease co-occurrence and the flow of patients among diagnoses. Finally, we define a strategy for the identification of trajectories that lead from one disease to another. The framework presented here has the potential to offer new insights for disease treatment and prevention in large health care systems.

Genetic Susceptibility to Acute Kidney Injury

Acute kidney injury (AKI) is a widely held concern related to a substantial burden of morbidity, mortality and expenditure in the healthcare system. AKI is not a simple illness but a complex conglomeration of syndromes that often occurs as part of other syndromes in its wide clinical spectrum of the disease. Genetic factors have been suggested as potentially responsible for its susceptibility and severity. As there is no current cure nor an effective treatment other than generally accepted supportive measures and renal replacement therapy, updated knowledge of the genetic implications may serve as a strategic tactic to counteract its dire consequences. Further understanding of the genetics that predispose AKI may shed light on novel approaches for the prevention and treatment of this condition. This review attempts to address the role of key genes in the appearance and development of AKI, providing not only a comprehensive update of the intertwined process involved but also identifying specific markers that could serve as precise targets for further AKI therapies.

A system-level analysis of patient disease trajectories based on clinical, phenotypic and molecular similarities

MOTIVATION

Incorporating the temporal dimension into multimorbidity studies has shown to be crucial for achieving a better understanding of the disease associations. Furthermore, due to the multifactorial nature of human disease, exploring disease associations from different perspectives can provide a holistic view to support the study of their aetiology.

RESULTS

In this work, a temporal systems-medicine approach is proposed for identifying time-dependent multimorbidity patterns from patient disease trajectories, by integrating data from electronic health records with genetic and phenotypic information. Specifically, the disease trajectories are clustered using an unsupervised algorithm based on dynamic time warping and three disease similarity metrics: clinical, genetic and phenotypic. An evaluation method is also presented for quantitatively assessing, in the different disease spaces, both the cluster homogeneity and the respective similarities between the associated diseases within individual trajectories. The latter can facilitate exploring the origin(s) in the identified disease patterns. The proposed integrative methodology can be applied to any longitudinal cohort and disease of interest. In this article, prostate cancer is selected as a use case of medical interest to demonstrate, for the first time, the identification of temporal disease multimorbidities in different disease spaces.

AVAILABILITY AND IMPLEMENTATION

https://gitlab.com/agiannoula/diseasetrajectories.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Decompression of Multimorbidity Along the Disease Trajectories of Diabetes Mellitus Patients

Multimorbidity, the presence of two or more diseases in a patient, is maybe the greatest health challenge for the aging populations of many high-income countries. One of the main drivers of multimorbidity is diabetes mellitus (DM) due to its large number of risk factors and complications. Yet, we currently have very limited understanding of how to quantify multimorbidity beyond a simple counting of diseases and thereby inform prevention and intervention strategies tailored to the needs of elderly DM patients. Here, we conceptualize multimorbidity as typical temporal progression patterns of multiple diseases, so-called trajectories, and develop a framework to perform a matched and sex-specific comparison between DM and non-diabetic patients. We find that these disease trajectories can be organized into a multi-level hierarchy in which DM patients progress from relatively healthy states with low mortality to high-mortality states characterized by cardiovascular diseases, chronic lower respiratory diseases, renal failure, and different combinations thereof. The same disease trajectories can be observed in non-diabetic patients, however, we find that DM patients typically progress at much higher rates along their trajectories. Comparing male and female DM patients, we find a general tendency that females progress faster toward high multimorbidity states than males, in particular along trajectories that involve obesity. Males, on the other hand, appear to progress faster in trajectories that combine heart diseases with cerebrovascular diseases. Our results show that prevention and efficient management of DM are key to achieve a compression of morbidity into higher patient ages. Multidisciplinary efforts involving clinicians as well as experts in machine learning and data visualization are needed to better understand the identified disease trajectories and thereby contribute to solving the current multimorbidity crisis in healthcare.

Theoretical and clinical disease and the biostatistical theory

Although concepts of disease have received much scrutiny, the benefits of distinguishing between theoretical and clinical disease-and what is meant by those terms-may not be as readily apparent. One way of characterizing the distinction between theoretical and clinical conceptions of disease is by relying on Boorse's biostatistical theory (BST) for a conception of theoretical disease. Clinical disease could then be defined as theoretical disease that is diagnosed. Explicating this distinction provides a useful extension of the BST. The benefits of this approach are clearly and non-normatively demarcating disease from non-disease, while allowing for values and purpose to determine what criteria are used in clinical practice to represent a disease's underlying dysfunction. Through discussion of a variety of medical conditions, including polycystic ovary syndrome and type 2 diabetes mellitus, I explore how the relationship between BST-based theoretical and clinical disease could make sense of various features of clinical practice and medical theory. It could do this by lending focus to a nuanced understanding of the pathophysiological defects present in disease and the means by which they are assessed. This could contribute to making sense of revised nosologies and diagnostic criteria.

Interpreting molecular similarity between patients as a determinant of disease comorbidity relationships

Comorbidity is a medical condition attracting increasing attention in healthcare and biomedical research. Little is known about the involvement of potential molecular factors leading to the emergence of a specific disease in patients affected by other conditions. We present here a disease interaction network inferred from similarities between patients’ molecular profiles, which significantly recapitulates epidemiologically documented comorbidities. Furthermore, we identify disease patient-subgroups that present different molecular similarities with other diseases, some of them opposing the general tendencies observed at the disease level. Analyzing the generated patient-subgroup network, we identify genes involved in such relations, together with drugs whose effects are potentially associated with the observed comorbidities. All the obtained associations are available at the disease PERCEPTION portal (http://disease-perception.bsc.es).

Disease comorbidity is attracting increasing attention, but the involvement of molecular factors in forecasting risk of a disease in the presence of other diseases is poorly understood. Here the authors build a disease interaction network based on gene expression profile and discover new comorbidity relationships in patient subgroups.

High-risk multimorbidity patterns on the road to cardiovascular mortality

BACKGROUND

Multimorbidity, the co-occurrence of two or more diseases in one patient, is a frequent phenomenon. Understanding how different diseases condition each other over the lifetime of a patient could significantly contribute to personalised prevention efforts. However, most of our current knowledge on the long-term development of the health of patients (their disease trajectories) is either confined to narrow time spans or specific (sets of) diseases. Here, we aim to identify decisive events that potentially determine the future disease progression of patients.

METHODS

Health states of patients are described by algorithmically identified multimorbidity patterns (groups of included or excluded diseases) in a population-wide analysis of 9,000,000 patient histories of hospital diagnoses observed over 17 years. Over time, patients might acquire new diagnoses that change their health state; they describe a disease trajectory. We measure the age- and sex-specific risks for patients that they will acquire certain sets of diseases in the future depending on their current health state.

RESULTS

In the present analysis, the population is described by a set of 132 different multimorbidity patterns. For elderly patients, we find 3 groups of multimorbidity patterns associated with low (yearly in-hospital mortality of 0.2-0.3%), medium (0.3-1%) and high in-hospital mortality (2-11%). We identify combinations of diseases that significantly increase the risk to reach the high-mortality health states in later life. For instance, in men (women) aged 50-59 diagnosed with diabetes and hypertension, the risk for moving into the high-mortality region within 1 year is increased by the factor of 1.96 ± 0.11 (2.60 ± 0.18) compared with all patients of the same age and sex, respectively, and by the factor of 2.09 ± 0.12 (3.04 ± 0.18) if additionally diagnosed with metabolic disorders.

CONCLUSIONS

Our approach can be used both to forecast future disease burdens, as well as to identify the critical events in the careers of patients which strongly determine their disease progression, therefore constituting targets for efficient prevention measures. We show that the risk for cardiovascular diseases increases significantly more in females than in males when diagnosed with diabetes, hypertension and metabolic disorders.

Recent advances in network medicine: From disease mechanisms to new treatment strategies

Conventional reductionist approaches have guided most of our understanding in disease diagnostic and treatment. However, most diseases are not consequence of perturbations in a single protein or metabolite, but rather of the effect that these perturbations have in their cellular context. The emerging field of network medicine offers a set of tools to explore molecular networks and to retrieve insights about mechanisms of different diseases. The study of the protein interactome, the map of physical interactions among human proteins, revealed that disease proteins tend to interact with each other, linking diseases to well-defined interactome neighborhoods. These disease-associated neighborhoods have been defined as disease modules, and they can uncover the biological significance of genes identified by genetic studies, reveal molecular mechanisms that connect different phenotypes, and help identify new pharmacological strategies for disease treatment. Therefore, network medicine offers a framework in which the complexity of different aspects of multiple sclerosis can be explored in an integrative fashion, which can ultimately provide insights about disease mechanisms and treatment.

Quantification of the resilience of primary care networks by stress testing the health care system

We shock a full-scale simulation model of a national health care system by locally removing health care providers. We measure resilience of the system in terms of how fast and to what extent it can recover its ability to deliver adequate health services to the population. The model is based on actual regional primary care networks in Austria, where all patients and physicians are represented as anonymized avatars that are calibrated with nationwide data. After removal of a critical fraction of physicians, networks generically undergo a transition from resilient to nonresilient behavior, where it is impossible to maintain coverage for all patients. These “stress tests” allow us to quantify regional health care resilience and identify systemically risky health care providers.

There are practically no quantitative tools for understanding how much stress a health care system can absorb before it loses its ability to provide care. We propose to measure the resilience of health care systems with respect to changes in the density of primary care providers. We develop a computational model on a 1-to-1 scale for a countrywide primary care sector based on patient-sharing networks. Nodes represent all primary care providers in a country; links indicate patient flows between them. The removal of providers could cause a cascade of patient displacements, as patients have to find alternative providers. The model is calibrated with nationwide data from Austria that includes almost all primary care contacts over 2 y. We assign 2 properties to every provider: the “CareRank” measures the average number of displacements caused by a provider’s removal (systemic risk) as well as the fraction of patients a provider can absorb when others default (systemic benefit). Below a critical number of providers, large-scale cascades of patient displacements occur, and no more providers can be found in a given region. We quantify regional resilience as the maximum fraction of providers that can be removed before cascading events prevent coverage for all patients within a district. We find considerable regional heterogeneity in the critical transition point from resilient to nonresilient behavior. We demonstrate that health care resilience cannot be quantified by physician density alone but must take into account how networked systems respond and restructure in response to shocks. The approach can identify systemically relevant providers.

The multiplex network of human diseases

Untangling the complex interplay between phenotype and genotype is crucial to the effective characterization and subtyping of diseases. Here we build and analyze the multiplex network of 779 human diseases, which consists of a genotype-based layer and a phenotype-based layer. We show that diseases with common genetic constituents tend to share symptoms, and uncover how phenotype information helps boost genotype information. Moreover, we offer a flexible classification of diseases that considers their molecular underpinnings alongside their clinical manifestations. We detect cohesive groups of diseases that have high intra-group similarity at both the molecular and the phenotypic level. Inspecting these disease communities, we demonstrate the underlying pathways that connect diseases mechanistically. We observe monogenic disorders grouped together with complex diseases for which they increase the risk factor. We propose potentially new disease associations that arise as a unique feature of the information flow within and across the two layers.

Nutritionally recommended food for semi- to strict vegetarian diets based on large-scale nutrient composition data

Diet design for vegetarian health is challenging due to the limited food repertoire of vegetarians. This challenge can be partially overcome by quantitative, data-driven approaches that utilise massive nutritional information collected for many different foods. Based on large-scale data of foods' nutrient compositions, the recent concept of nutritional fitness helps quantify a nutrient balance within each food with regard to satisfying daily nutritional requirements. Nutritional fitness offers prioritisation of recommended foods using the foods' occurrence in nutritionally adequate food combinations. Here, we systematically identify nutritionally recommendable foods for semi- to strict vegetarian diets through the computation of nutritional fitness. Along with commonly recommendable foods across different diets, our analysis reveals favourable foods specific to each diet, such as immature lima beans for a vegan diet as an amino acid and choline source, and mushrooms for ovo-lacto vegetarian and vegan diets as a vitamin D source. Furthermore, we find that selenium and other essential micronutrients can be subject to deficiency in plant-based diets, and suggest nutritionally-desirable dietary patterns. We extend our analysis to two hypothetical scenarios of highly personalised, plant-based methionine-restricted diets. Our nutrient-profiling approach may provide a useful guide for designing different types of personalised vegetarian diets.

Trajectories of functional decline in older adults with neuropsychiatric and cardiovascular multimorbidity: A Swedish cohort study

BACKGROUND

Functional decline is a strong health determinant in older adults, and chronic diseases play a major role in this age-related phenomenon. In this study, we explored possible clinical pathways underlying functional heterogeneity in older adults by quantifying the impact of cardiovascular (CV) and neuropsychiatric (NP) chronic diseases and their co-occurrence on trajectories of functional decline.

METHODS AND FINDINGS

We studied 2,385 people ≥60 years (range 60-101 years) participating in the Swedish National study of Aging and Care in Kungsholmen (SNAC-K). Participants underwent clinical examination at baseline (2001-2004) and every 3 or 6 years for up to 9 years. We grouped participants on the basis of 7 mutually exclusive clinical patterns of 0, 1, or more CV and NP diseases and their co-occurrence, from a group without any CV and NP disease to a group characterised by the presence of CV or NP multimorbidity, accompanied by at least 1 other CV or NP disorder. The group with no CV and/or NP diseases served as the reference group. Functional decline was estimated over 9 years of follow-up by measuring mobility (walking speed, m/s) and independence (ability to carry out six activities of daily living [ADL]). Mixed-effect linear regression models were used (1) to explore the individual-level prognostic predictivity of the different CV and NP clinical patterns at baseline and (2) to quantify the association between the clinical patterns and functional decline at the group level by entering the clinical patterns as time-varying measures. During the 9-year follow-up, participants with multiple CV and NP diseases had the steepest decline in walking speed (up to 0.7 m/s; p < 0.001) and ADL independence (up to three impairments in ADL, p < 0.001) (reference group: participants without any CV and NP disease). When the clinical patterns were analyzed as time varying, isolated CV multimorbidity impacted only walking speed (β -0.1; p < 0.001). Conversely, all the clinical patterns that included at least 1 NP disease were significantly associated with decline in both walking speed (β -0.21--0.08; p < 0.001) and ADL independence (β -0.27--0.06; p < 0.05). Groups with the most complex clinical patterns had 5%-20% lower functioning at follow-up than the reference group. Key limitations of the study include that we did not take into account the specific weight of single diseases and their severity and that the exclusion of participants with less than 2 assessments may have led to an underestimation of the tested associations.

CONCLUSIONS

In older adults, different patterns of CV and NP morbidity lead to different trajectories of functional decline over time, a finding that explains part of the heterogeneity observed in older adults' functionality. NP diseases, alone or in association, are prevalent and major determinants of functional decline, whereas isolated CV multimorbidity is associated only with declines in mobility.