Exploring the molecular basis of age-related disease comorbidities using a multi-omics graphical model

Sex and aging signatures of proteomics in human cerebrospinal fluid identify distinct clusters linked to neurodegeneration

Sex and age are major risk factors for chronic diseases. Recent studies examining age-related molecular changes in plasma provided insights into age-related disease biology. Cerebrospinal fluid (CSF) proteomics can provide additional insights into brain aging and neurodegeneration. By comprehensively examining 7,006 aptamers targeting 6,139 proteins in CSF obtained from 660 healthy individuals aged from 43 to 91 years old, we subsequently identified significant sex and aging effects on 5,097 aptamers in CSF. Many of these effects on CSF proteins had different magnitude or even opposite direction as those on plasma proteins, indicating distinctive CSF-specific signatures. Network analysis of these CSF proteins revealed not only modules associated with healthy aging but also modules showing sex differences. Through subsequent analyses, several modules were highlighted for their proteins implicated in specific diseases. Module 2 and 6 were enriched for many aging diseases including those in the circulatory systems, immune mechanisms, and neurodegeneration. Together, our findings fill a gap of current aging research and provide mechanistic understanding of proteomic changes in CSF during a healthy lifespan and insights for brain aging and diseases.

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.

Childhood exposure to non-persistent endocrine disrupting chemicals and multi-omic profiles: A panel study

BACKGROUND

Individuals are exposed to environmental pollutants with endocrine disrupting activity (endocrine disruptors, EDCs) and the early stages of life are particularly susceptible to these exposures. Previous studies have focused on identifying molecular signatures associated with EDCs, but none have used repeated sampling strategy and integrated multiple omics. We aimed to identify multi-omic signatures associated with childhood exposure to non-persistent EDCs.

METHODS

We used data from the HELIX Child Panel Study, which included 156 children aged 6 to 11. Children were followed for one week, in two time periods. Twenty-two non-persistent EDCs (10 phthalate, 7 phenol, and 5 organophosphate pesticide metabolites) were measured in two weekly pools of 15 urine samples each. Multi-omic profiles (methylome, serum and urinary metabolome, proteome) were measured in blood and in a pool urine samples. We developed visit-specific Gaussian Graphical Models based on pairwise partial correlations. The visit-specific networks were then merged to identify reproducible associations. Independent biological evidence was systematically sought to confirm some of these associations and assess their potential health implications.

RESULTS

950 reproducible associations were found among which 23 were direct associations between EDCs and omics. For 9 of them, we were able to find corroborating evidence from previous literature: DEP - serotonin, OXBE - cg27466129, OXBE - dimethylamine, triclosan - leptin, triclosan - serotonin, MBzP - Neu5AC, MEHP - cg20080548, oh-MiNP - kynurenine, oxo-MiNP - 5-oxoproline. We used these associations to explore possible mechanisms between EDCs and health outcomes, and found links to health outcomes for 3 analytes: serotonin and kynurenine in relation to neuro-behavioural development, and leptin in relation to obesity and insulin resistance.

CONCLUSIONS

This multi-omics network analysis at two time points identified biologically relevant molecular signatures related to non-persistent EDC exposure in childhood, suggesting pathways related to neurological and metabolic outcomes.

The landscape of aging

Aging is characterized by a progressive deterioration of physiological integrity, leading to impaired functional ability and ultimately increased susceptibility to death. It is a major risk factor for chronic human diseases, including cardiovascular disease, diabetes, neurological degeneration, and cancer. Therefore, the growing emphasis on “healthy aging” raises a series of important questions in life and social sciences. In recent years, there has been unprecedented progress in aging research, particularly the discovery that the rate of aging is at least partly controlled by evolutionarily conserved genetic pathways and biological processes. In an attempt to bring full-fledged understanding to both the aging process and age-associated diseases, we review the descriptive, conceptual, and interventive aspects of the landscape of aging composed of a number of layers at the cellular, tissue, organ, organ system, and organismal levels.

Emerging approaches to multiple chronic condition assessment

Older adults experience a higher prevalence of multiple chronic conditions (MCCs). Establishing the presence and pattern of MCCs in individuals or populations is important for healthcare delivery, research, and policy. This report describes four emerging approaches and discusses their potential applications for enhancing assessment, treatment, and policy for the aging population. The National Institutes of Health convened a 2-day panel workshop of experts in 2018. Four emerging models were identified by the panel, including classification and regression tree (CART), qualifying comorbidity sets (QCS), the multimorbidity index (MMI), and the application of omics to network medicine. Future research into models of multiple chronic condition assessment may improve understanding of the epidemiology, diagnosis, and treatment of older persons.

Combining Molecular, Imaging, and Clinical Data Analysis for Predicting Cancer Prognosis

Simple Summary

The rise of Big Data, the widespread use of Machine Learning, and the cheapening of omics techniques have allowed for the creation of more sophisticated and accurate models in biomedical research. This article presents the state-of-the-art predictive models of cancer prognosis that use multimodal data, considering clinical, molecular (omics and non-omics), and image data. The subject of study, the data modalities used, the data processing and modelling methods applied, the validation strategies involved, the integration strategies encompassed, and the evolution of prognostic predictive models are discussed. Finally, we discuss challenges and opportunities in this field of cancer research, with great potential impact on the clinical management of patients and, by extension, on the implementation of personalised and precision medicine.

Abstract

Cancer is one of the most detrimental diseases globally. Accordingly, the prognosis prediction of cancer patients has become a field of interest. In this review, we have gathered 43 state-of-the-art scientific papers published in the last 6 years that built cancer prognosis predictive models using multimodal data. We have defined the multimodality of data as four main types: clinical, anatomopathological, molecular, and medical imaging; and we have expanded on the information that each modality provides. The 43 studies were divided into three categories based on the modelling approach taken, and their characteristics were further discussed together with current issues and future trends. Research in this area has evolved from survival analysis through statistical modelling using mainly clinical and anatomopathological data to the prediction of cancer prognosis through a multi-faceted data-driven approach by the integration of complex, multimodal, and high-dimensional data containing multi-omics and medical imaging information and by applying Machine Learning and, more recently, Deep Learning techniques. This review concludes that cancer prognosis predictive multimodal models are capable of better stratifying patients, which can improve clinical management and contribute to the implementation of personalised medicine as well as provide new and valuable knowledge on cancer biology and its progression.

Risk Prediction of Cardiovascular Events by Exploration of Molecular Data with Explainable Artificial Intelligence

Cardiovascular diseases (CVD) annually take almost 18 million lives worldwide. Most lethal events occur months or years after the initial presentation. Indeed, many patients experience repeated complications or require multiple interventions (recurrent events). Apart from affecting the individual, this leads to high medical costs for society. Personalized treatment strategies aiming at prediction and prevention of recurrent events rely on early diagnosis and precise prognosis. Complementing the traditional environmental and clinical risk factors, multi-omics data provide a holistic view of the patient and disease progression, enabling studies to probe novel angles in risk stratification. Specifically, predictive molecular markers allow insights into regulatory networks, pathways, and mechanisms underlying disease. Moreover, artificial intelligence (AI) represents a powerful, yet adaptive, framework able to recognize complex patterns in large-scale clinical and molecular data with the potential to improve risk prediction. Here, we review the most recent advances in risk prediction of recurrent cardiovascular events, and discuss the value of molecular data and biomarkers for understanding patient risk in a systems biology context. Finally, we introduce explainable AI which may improve clinical decision systems by making predictions transparent to the medical practitioner.

Variability of multi-omics profiles in a population-based child cohort

BACKGROUND

Multiple omics technologies are increasingly applied to detect early, subtle molecular responses to environmental stressors for future disease risk prevention. However, there is an urgent need for further evaluation of stability and variability of omics profiles in healthy individuals, especially during childhood.

METHODS

We aimed to estimate intra-, inter-individual and cohort variability of multi-omics profiles (blood DNA methylation, gene expression, miRNA, proteins and serum and urine metabolites) measured 6 months apart in 156 healthy children from five European countries. We further performed a multi-omics network analysis to establish clusters of co-varying omics features and assessed the contribution of key variables (including biological traits and sample collection parameters) to omics variability.

RESULTS

All omics displayed a large range of intra- and inter-individual variability depending on each omics feature, although all presented a highest median intra-individual variability. DNA methylation was the most stable profile (median 37.6% inter-individual variability) while gene expression was the least stable (6.6%). Among the least stable features, we identified 1% cross-omics co-variation between CpGs and metabolites (e.g. glucose and CpGs related to obesity and type 2 diabetes). Explanatory variables, including age and body mass index (BMI), explained up to 9% of serum metabolite variability.

CONCLUSIONS

Methylation and targeted serum metabolomics are the most reliable omics to implement in single time-point measurements in large cross-sectional studies. In the case of metabolomics, sample collection and individual traits (e.g. BMI) are important parameters to control for improved comparability, at the study design or analysis stage. This study will be valuable for the design and interpretation of epidemiological studies that aim to link omics signatures to disease, environmental exposures, or both.

Using machine learning approaches for multi-omics data analysis: A review

With the development of modern high-throughput omic measurement platforms, it has become essential for biomedical studies to undertake an integrative (combined) approach to fully utilise these data to gain insights into biological systems. Data from various omics sources such as genetics, proteomics, and metabolomics can be integrated to unravel the intricate working of systems biology using machine learning-based predictive algorithms. Machine learning methods offer novel techniques to integrate and analyse the various omics data enabling the discovery of new biomarkers. These biomarkers have the potential to help in accurate disease prediction, patient stratification and delivery of precision medicine. This review paper explores different integrative machine learning methods which have been used to provide an in-depth understanding of biological systems during normal physiological functioning and in the presence of a disease. It provides insight and recommendations for interdisciplinary professionals who envisage employing machine learning skills in multi-omics studies.

Integration strategies of multi-omics data for machine learning analysis

Increased availability of high-throughput technologies has generated an ever-growing number of omics data that seek to portray many different but complementary biological layers including genomics, epigenomics, transcriptomics, proteomics, and metabolomics. New insight from these data have been obtained by machine learning algorithms that have produced diagnostic and classification biomarkers. Most biomarkers obtained to date however only include one omic measurement at a time and thus do not take full advantage of recent multi-omics experiments that now capture the entire complexity of biological systems.

Multi-omics data integration strategies are needed to combine the complementary knowledge brought by each omics layer. We have summarized the most recent data integration methods/ frameworks into five different integration strategies: early, mixed, intermediate, late and hierarchical. In this mini-review, we focus on challenges and existing multi-omics integration strategies by paying special attention to machine learning applications.

Identifying molecular targets for reverse aging using integrated network analysis of transcriptomic and epigenomic changes during aging

Aging is associated with widespread physiological changes, including skeletal muscle weakening, neuron system degeneration, hair loss, and skin wrinkling. Previous studies have identified numerous molecular biomarkers involved in these changes, but their regulatory mechanisms and functional repercussions remain elusive. In this study, we conducted next-generation sequencing of DNA methylation and RNA sequencing of blood samples from 51 healthy adults between 20 and 74 years of age and identified aging-related epigenetic and transcriptomic biomarkers. We also identified candidate molecular targets that can reversely regulate the transcriptomic biomarkers of aging by reconstructing a gene regulatory network model and performing signal flow analysis. For validation, we screened public experimental data including gene expression profiles in response to thousands of chemical perturbagens. Despite insufficient data on the binding targets of perturbagens and their modes of action, curcumin, which reversely regulated the biomarkers in the experimental dataset, was found to bind and inhibit JUN, which was identified as a candidate target via signal flow analysis. Collectively, our results demonstrate the utility of a network model for integrative analysis of omics data, which can help elucidate inter-omics regulatory mechanisms and develop therapeutic strategies against aging.

Clinical epigenetics settings for cancer and cardiovascular diseases: real-life applications of network medicine at the bedside

Despite impressive efforts invested in epigenetic research in the last 50 years, clinical applications are still lacking. Only a few university hospital centers currently use epigenetic biomarkers at the bedside. Moreover, the overall concept of precision medicine is not widely recognized in routine medical practice and the reductionist approach remains predominant in treating patients affected by major diseases such as cancer and cardiovascular diseases. By its' very nature, epigenetics is integrative of genetic networks. The study of epigenetic biomarkers has led to the identification of numerous drugs with an increasingly significant role in clinical therapy especially of cancer patients. Here, we provide an overview of clinical epigenetics within the context of network analysis. We illustrate achievements to date and discuss how we can move from traditional medicine into the era of network medicine (NM), where pathway-informed molecular diagnostics will allow treatment selection following the paradigm of precision medicine.

Versatile knowledge guided network inference method for prioritizing key regulatory factors in multi-omics data

Constantly decreasing costs of high-throughput profiling on many molecular levels generate vast amounts of multi-omics data. Studying one biomedical question on two or more omic levels provides deeper insights into underlying molecular processes or disease pathophysiology. For the majority of multi-omics data projects, the data analysis is performed level-wise, followed by a combined interpretation of results. Hence the full potential of integrated data analysis is not leveraged yet, presumably due to the complexity of the data and the lacking toolsets. We propose a versatile approach, to perform a multi-level fully integrated analysis: The Knowledge guIded Multi-Omics Network inference approach, KiMONo (https://github.com/cellmapslab/kimono). KiMONo performs network inference by using statistical models for combining omics measurements coupled to a powerful knowledge-guided strategy exploiting prior information from existing biological sources. Within the resulting multimodal network, nodes represent features of all input types e.g. variants and genes while edges refer to knowledge-supported and statistically derived associations. In a comprehensive evaluation, we show that our method is robust to noise and exemplify the general applicability to the full spectrum of multi-omics data, demonstrating that KiMONo is a powerful approach towards leveraging the full potential of data sets for detecting biomarker candidates.

Metabolomics: small molecules that matter more

Metabolomics, an analytical study with high-throughput profiling, helps to understand interactions within a biological system. Small molecules, called metabolites or metabolomes with the size of <1500 Da, depict the status of a biological system in a different manner. Currently, we are in need to globally analyze the metabolome and the pathways involved in healthy, as well as diseased conditions, for possible therapeutic applications. Metabolome analysis has revealed high-abundance molecules during different conditions such as diet, environmental stress, microbiota, and disease and treatment states. As a result, it is hard to understand the complete and stable network of metabolites of a biological system. This review helps readers know the available techniques to study metabolomics in addition to other major omics such as genomics, transcriptomics, and proteomics. This review also discusses the metabolomics in various pathological conditions and the importance of metabolomics in therapeutic applications.

Integrative analyses of TEDDY Omics data reveal lipid metabolism abnormalities, increased intracellular ROS and heightened inflammation prior to autoimmunity for type 1 diabetes

BACKGROUND

The Environmental Determinants of Diabetes in the Young (TEDDY) is a prospective birth cohort designed to study type 1 diabetes (T1D) by following children with high genetic risk. An integrative multi-omics approach was used to evaluate islet autoimmunity etiology, identify disease biomarkers, and understand progression over time.

RESULTS

We identify a multi-omics signature that was predictive of islet autoimmunity (IA) as early as 1 year before seroconversion. At this time, abnormalities in lipid metabolism, decreased capacity for nutrient absorption, and intracellular ROS accumulation are detected in children progressing towards IA. Additionally, extracellular matrix remodeling, inflammation, cytotoxicity, angiogenesis, and increased activity of antigen-presenting cells are observed, which may contribute to beta cell destruction. Our results indicate that altered molecular homeostasis is present in IA-developing children months before the actual detection of islet autoantibodies, which opens an interesting window of opportunity for therapeutic intervention.

CONCLUSIONS

The approach employed herein for assessment of the TEDDY cohort showcases the utilization of multi-omics data for the modeling of complex, multifactorial diseases, like T1D.

Multi-omics integration in biomedical research - A metabolomics-centric review

Recent advances in high-throughput technologies have enabled the profiling of multiple layers of a biological system, including DNA sequence data (genomics), RNA expression levels (transcriptomics), and metabolite levels (metabolomics). This has led to the generation of vast amounts of biological data that can be integrated in so-called multi-omics studies to examine the complex molecular underpinnings of health and disease. Integrative analysis of such datasets is not straightforward and is particularly complicated by the high dimensionality and heterogeneity of the data and by the lack of universal analysis protocols. Previous reviews have discussed various strategies to address the challenges of data integration, elaborating on specific aspects, such as network inference or feature selection techniques. Thereby, the main focus has been on the integration of two omics layers in their relation to a phenotype of interest. In this review we provide an overview over a typical multi-omics workflow, focusing on integration methods that have the potential to combine metabolomics data with two or more omics. We discuss multiple integration concepts including data-driven, knowledge-based, simultaneous and step-wise approaches. We highlight the application of these methods in recent multi-omics studies, including large-scale integration efforts aiming at a global depiction of the complex relationships within and between different biological layers without focusing on a particular phenotype.

Glycosylation and Aging

Human lifespan has increased significantly in the last 200 years, emphasizing our need to age healthily. Insights into molecular mechanisms of aging might allow us to slow down its rate or even revert it. Similar to aging, glycosylation is regulated by an intricate interplay of genetic and environmental factors. The dynamics of glycopattern variation during aging has been mostly explored for plasma/serum and immunoglobulin G (IgG) N-glycome, as we describe thoroughly in this chapter. In addition, we discuss the potential functional role of agalactosylated IgG glycans in aging, through modulation of inflammation level, as proposed by the concept of inflammaging. We also comment on the potential to use the plasma/serum and IgG N-glycome as a biomarker of healthy aging and on the interventions that modulate the IgG glycopattern. Finally, we discuss the current knowledge about animal models for human plasma/serum and IgG glycosylation and mention other, less explored, instances of glycopattern changes during organismal aging and cellular senescence.

Exercise-Induced Myokines can Explain the Importance of Physical Activity in the Elderly: An Overview

Physical activity has been found to aid the maintenance of health in the elderly. Exercise-induced skeletal muscle contractions lead to the production and secretion of many small proteins and proteoglycan peptides called myokines. Thus, studies on myokines are necessary for ensuring the maintenance of skeletal muscle health in the elderly. This review summarizes 13 myokines regulated by physical activity that are affected by aging and aims to understand their potential roles in metabolic diseases. We categorized myokines into two groups based on regulation by aerobic and anaerobic exercise. With aging, the secretion of apelin, β-aminoisobutyric acid (BAIBA), bone morphogenetic protein 7 (BMP-7), decorin, insulin-like growth factor 1 (IGF-1), interleukin-15 (IL-15), irisin, stromal cell-derived factor 1 (SDF-1), sestrin, secreted protein acidic rich in cysteine (SPARC), and vascular endothelial growth factor A (VEGF-A) decreased, while that of IL-6 and myostatin increased. Aerobic exercise upregulates apelin, BAIBA, IL-15, IL-6, irisin, SDF-1, sestrin, SPARC, and VEGF-A expression, while anaerobic exercise upregulates BMP-7, decorin, IGF-1, IL-15, IL-6, irisin, and VEGF-A expression. Myostatin is downregulated by both aerobic and anaerobic exercise. This review provides a rationale for developing exercise programs or interventions that maintain a balance between aerobic and anaerobic exercise in the elderly.

Gaussian and Mixed Graphical Models as (multi-)omics data analysis tools

Gaussian Graphical Models (GGMs) are tools to infer dependencies between biological variables. Popular applications are the reconstruction of gene, protein, and metabolite association networks. GGMs are an exploratory research tool that can be useful to discover interesting relations between genes (functional clusters) or to identify therapeutically interesting genes, but do not necessarily infer a network in the mechanistic sense. Although GGMs are well investigated from a theoretical and applied perspective, important extensions are not well known within the biological community. GGMs assume, for instance, multivariate normal distributed data. If this assumption is violated Mixed Graphical Models (MGMs) can be the better choice. In this review, we provide the theoretical foundations of GGMs, present extensions such as MGMs or multi-class GGMs, and illustrate how those methods can provide insight in biological mechanisms. We summarize several applications and present user-friendly estimation software. This article is part of a Special Issue entitled: Transcriptional Profiles and Regulatory Gene Networks edited by Dr. Dr. Federico Manuel Giorgi and Dr. Shaun Mahony.

Ageotypes: Distinct Biomolecular Trajectories in Human Aging

Many studies have demonstrated that biological age (BA) varies significantly among individuals of similar chronological age. A recent study by Ahadi et al. used longitudinal and deep multi-omic profiling to identify individuals with distinct BA phenotypes or 'ageotypes'. These ageotypes open new avenues to creating diagnostic and treatment strategies that may slow the aging process based on the unique biochemistry of each individual.

Five-year Clinical Outcomes of CAD Patients Complicated with Diabetes after StentBoost-optimized Percutaneous Coronary Intervention

Objective To evaluate the instant effects and five-year clinical outcomes of coronary artery disease patients complicated with diabetes mellitus after StentBoost-optimized percutaneous coronary intervention (PCI). Methods From March 2009 to July 2010, 184 patients undergoing PCI at our hospital were found stent underexpansion or malapposition by StentBoost after stents implantation and were divided into the diabetic (n=73, 39.67%) and the non-diabetic group (n=111, 60.33%). All patients received StentBoost-guided post-dilatation after stent implantation. The instant procedural results were measured and clinical outcome after five-year follow-up was analyzed in each group. Between-group comparisons were performed using Chi-square test or Student's t test. Multivariate logistic regression analysis was carried out to reveal the independent predictors for long-term clinical outcomes of StentBoost-optimized PCI . Results After StentBoost-guided post-dilatation, the minimum diameter (MinLD), maximum diameter (MaxLD) and average diameter in both groups increased significantly than before (P<0.001), the (MaxLD-MinLD)/MaxLD ratio and the in-stent residual stenosis decreased accordingly (P<0.001). The five-year follow-up showed similar mortality rate (4.92% vs. 2.86%, P=0.67) and major adverse cardiac event rate (11.48% vs. 11.43%, P = 1.0) between the diabetic and the non-diabetic group, whereas the recurrence of angina pectoris was higher in the diabetic group compared to the non-diabetic group (47.54% vs. 29.52%; P=0.02). A multivariate logistic regression analysis revealed that age and left ventricular ejection fraction rather than diabetes mellitus were independent predictors for long-term clinical outcomes. Conclusions StentBoost could effectively improve instant PCI results; the long-term clinical outcomes of StentBoost-optimized PCI were similar between diabetic and non-diabetic patients. Age and left ventricular ejection fraction were the independent predictors for long-term clinical outcomes.

Sex differences in the association between salivary telomere length and multimorbidity within the US Health & Retirement Study

Background

Telomere length is associated with several physical and mental health conditions, but whether it is a marker of multimorbidity is unclear. We investigated associations between telomere length and multimorbidity by sex.

Methods

Data from adults (N = 5,495) aged ≥50 years were taken from the US Health and Retirement Study (2008–14). Telomere length was measured in 2008 from salivary samples. The cross-sectional associations between telomere length and eight chronic health conditions were explored using logistic regression, adjusting for confounders and stratified by sex. Logistic, ordinal and multinomial regression models were calculated to explore relationships between telomere length and multimorbidity (using a binary variable and a sum of the number of health conditions) and the type of multimorbidity (no multimorbidity, physical multimorbidity, or multimorbidity including psychiatric problems). Using multilevel logistic regression, prospective relationships between telomere length and incident multimorbidity were also explored.

Results

In cross-sectional analyses, longer telomeres were associated with reduced likelihood of lung disease and psychiatric problems among men, but not women. Longer telomeres were associated with lower risk of multimorbidity that included psychiatric problems among men (OR=0.521, 95% CI: 0.284 to 0.957), but not women (OR=1.188, 95% CI: 0.771 to 1.831). Prospective analyses suggested little association between telomere length and the onset of multimorbidity in men (OR=1.378, 95% CI: 0.931 to 2.038) nor women (OR=1.224, 95% CI: 0.825 to 1.815).

Conclusions

Although telomere length does not appear to be a biomarker of overall multimorbidity, further exploration of the relationships is merited particularly for multimorbidity including psychiatric conditions among men.

Health and Aging: Unifying Concepts, Scores, Biomarkers and Pathways

Despite increasing research efforts, there is a lack of consensus on defining aging or health. To understand the underlying processes, and to foster the development of targeted interventions towards increasing one's health, there is an urgent need to find a broadly acceptable and useful definition of health, based on a list of (molecular) features; to operationalize features of health so that it can be measured; to identify predictive biomarkers and (molecular) pathways of health; and to suggest interventions, such as nutrition and exercise, targeted at putative causal pathways and processes. Based on a survey of the literature, we propose to define health as a state of an individual characterized by the core features of physiological, cognitive, physical and reproductive function, and a lack of disease. We further define aging as the aggregate of all processes in an individual that reduce its wellbeing, that is, its health or survival or both. We define biomarkers of health by their attribute of predicting future health better than chronological age. We define healthspan pathways as molecular features of health that relate to each other by belonging to the same molecular pathway. Our conceptual framework may integrate diverse operationalizations of health and guide precision prevention efforts.

Fine-Mapping of the Human Blood Plasma N-Glycome onto Its Proteome

Most human proteins are glycosylated. Attachment of complex oligosaccharides to the polypeptide part of these proteins is an integral part of their structure and function and plays a central role in many complex disorders. One approach towards deciphering this human glycan code is to study natural variation in experimentally well characterized samples and cohorts. High-throughput capable large-scale methods that allow for the comprehensive determination of blood circulating proteins and their glycans have been recently developed, but so far, no study has investigated the link between both traits. Here we map for the first time the blood plasma proteome to its matching N-glycome by correlating the levels of 1116 blood circulating proteins with 113 N-glycan traits, determined in 344 samples from individuals of Arab, South-Asian, and Filipino descent, and then replicate our findings in 46 subjects of European ancestry. We report protein-specific N-glycosylation patterns, including a correlation of core fucosylated structures with immunoglobulin G (IgG) levels, and of trisialylated, trigalactosylated, and triantennary structures with heparin cofactor 2 (SERPIND2). Our study reveals a detailed picture of protein N-glycosylation and suggests new avenues for the investigation of its role and function in the associated complex disorders.

Integration of Metabolomic and Other Omics Data in Population-Based Study Designs: An Epidemiological Perspective

It is not controversial that study design considerations and challenges must be addressed when investigating the linkage between single omic measurements and human phenotypes. It follows that such considerations are just as critical, if not more so, in the context of multi-omic studies. In this review, we discuss (1) epidemiologic principles of study design, including selection of biospecimen source(s) and the implications of the timing of sample collection, in the context of a multi-omic investigation, and (2) the strengths and limitations of various techniques of data integration across multi-omic data types that may arise in population-based studies utilizing metabolomic data.

Inferring Interaction Networks From Multi-Omics Data

A major goal in systems biology is a comprehensive description of the entirety of all complex interactions between different types of biomolecules—also referred to as the interactome—and how these interactions give rise to higher, cellular and organism level functions or diseases. Numerous efforts have been undertaken to define such interactomes experimentally, for example yeast-two-hybrid based protein-protein interaction networks or ChIP-seq based protein-DNA interactions for individual proteins. To complement these direct measurements, genome-scale quantitative multi-omics data (transcriptomics, proteomics, metabolomics, etc.) enable researchers to predict novel functional interactions between molecular species. Moreover, these data allow to distinguish relevant functional from non-functional interactions in specific biological contexts. However, integration of multi-omics data is not straight forward due to their heterogeneity. Numerous methods for the inference of interaction networks from homogeneous functional data exist, but with the advent of large-scale paired multi-omics data a new class of methods for inferring comprehensive networks across different molecular species began to emerge. Here we review state-of-the-art techniques for inferring the topology of interaction networks from functional multi-omics data, encompassing graphical models with multiple node types and quantitative-trait-loci (QTL) based approaches. In addition, we will discuss Bayesian aspects of network inference, which allow for leveraging already established biological information such as known protein-protein or protein-DNA interactions, to guide the inference process.

Untangling Aging Using Dynamic, Organism-Level Phenotypic Networks

Research on aging requires the ability to measure aging, and therein lies a challenge: it is impossible to measure every molecular, cellular, and physiological change that develops over time, but it is difficult to prioritize phenotypes for measurement because it is unclear which biological changes should be considered aspects of aging and, further, which species and environments exhibit "real aging." Here, I propose a strategy to address this challenge: rather than classify phenotypes as "real aging" or not, conceptualize aging as the set of all age-dependent phenotypes and appreciate that this set and its underlying mechanisms may vary by population. Use automated phenotyping technologies to measure as many age-dependent phenotypes as possible within individuals over time, prioritizing organism-level (i.e., physiological) phenotypes in order to enrich for health relevance. Use those high-dimensional phenotypic data to construct dynamic networks that allow aging to be studied with unprecedented sophistication and rigor.

Genome-Wide Association Studies of Metabolite Concentrations (mGWAS): Relevance for Nephrology

Metabolites are small molecules that are intermediates or products of metabolism, many of which are freely filtered by the kidneys. In addition, the kidneys have a central role in metabolite anabolism and catabolism, as well as in active metabolite reabsorption and/or secretion during tubular passage. This review article illustrates how the coupling of genomics and metabolomics in genome-wide association analyses of metabolites can be used to illuminate mechanisms underlying human metabolism, with a special focus on insights relevant to nephrology. First, genetic susceptibility loci for reduced kidney function and chronic kidney disease (CKD) were reviewed systematically for their associations with metabolite concentrations in metabolomics studies of blood and urine. Second, kidney function and CKD-associated metabolites reported from observational studies were interrogated for metabolite-associated genetic variants to generate and discuss complementary insights. Finally, insights originating from the simultaneous study of both blood and urine or by modeling intermetabolite relationships are summarized. We also discuss methodologic questions related to the study of metabolite concentrations in urine as well as among CKD patients. In summary, genome-wide association analyses of metabolites using metabolite concentrations quantified from blood and/or urine are a promising avenue of research to illuminate physiological and pathophysiological functions of the kidney.

The age-related loss of skeletal muscle mass and function: Measurement and physiology of muscle fibre atrophy and muscle fibre loss in humans

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Loss of muscle mass with age is due to atrophy and loss of individual muscle fibres.

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Anabolic resistance is fundamental in age-related fibre atrophy.

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Fibre loss is associated with denervation and remodelling of motor units.

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The plasticity of both factors should be considered in future research.

Age-related loss of skeletal muscle mass and function, sarcopenia, is associated with physical frailty and increased risk of morbidity (chronic diseases), in addition to all-cause mortality. The loss of muscle mass occurs incipiently from middle-age (∼1%/year), and in severe instances can lead to a loss of ∼50% by the 8–9th decade of life. This review will focus on muscle deterioration with ageing and highlight the two underpinning mechanisms regulating declines in muscle mass and function: muscle fibre atrophy and muscle fibre loss (hypoplasia) – and their measurement. The mechanisms of muscle fibre atrophy in humans relate to imbalances in muscle protein synthesis (MPS) and breakdown (MPB); however, since there is limited evidence for basal alterations in muscle protein turnover, it would appear that “anabolic resistance” to fundamental environmental cues regulating diurnal muscle homeostasis (namely physical activity and nutrition), underlie age-related catabolic perturbations in muscle proteostasis. While the ‘upstream’ drivers of the desensitization of aged muscle to anabolic stimuli are poorly defined, they most likely relate to impaired efficiency of the conversion of nutritional/exercise stimuli into signalling impacting mRNA translation and proteolysis. Additionally, loss of muscle fibres has been shown in cadaveric studies using anatomical fibre counts, and from iEMG studies demonstrating motor unit loss, albeit with few molecular investigations of this in humans. We suggest that defining countermeasures against sarcopenia requires improved understandings of the co-ordinated regulation of muscle fibre atrophy and fibre loss, which are likely to be inextricably linked.

Physiological markers and multimorbidity: A systematic review

Background:

Multimorbidity is the co-occurrence of two or more diseases in the same individual. One method to identify this condition at an early stage is the use of specific markers for various combinations of morbidities. Nonetheless, evidence related to physiological markers in multimorbidity is limited.

Objective:

The aim was to perform a systematic review to identify physiological markers associated with multimorbidity.

Design:

Articles available on PubMed, Register of Controlled Trials, Academic Search Premier, CINAHL, Scopus, SocINDEX, Web of Science, LILACS, and SciELO, from their inception to May 2018, were systematically searched and reviewed. The project was registered in PROSPERO under the number CRD42017055522.

Results:

The systematic search identified 922 papers. After evaluation, 18 articles were included in the full review reporting at least one physiological marker in coexisting diseases or which are strongly associated with the presence of multimorbidity in the future. Only five of these studies examined multimorbidity in general, identifying five physiological markers associated with multimorbidity, namely, dehydroepiandrosterone sulfate (DHEAS), interleukin 6 (IL-6), C-reactive protein (CRP), lipoprotein (Lp), and cystatin C (Cyst-C).

Conclusions:

There is a paucity of studies related to physiological markers in multimorbidity. DHEAS, IL-6, CRP, Lp, and Cyst-C could be the initial focus for further investigation of physiological markers related to multimorbidity.

Systems Medicine Disease: Disease Classification and Scalability Beyond Networks and Boundary Conditions

In order to accommodate the forthcoming wealth of health and disease related information, from genome to body sensors to population and the environment, the approach to disease description and definition demands re-examination. Traditional classification methods remain trapped by history; to provide the descriptive features that are required for a comprehensive description of disease, systems science, which realizes dynamic processes, adaptive response, and asynchronous communication channels, must be applied (Wolkenhauer et al., 2013). When Disease is viewed beyond the thresholds of lines and threshold boundaries, disease definition is not only the result of reductionist, mechanistic categories which reluctantly face re-composition. Disease is process and synergy as the characteristics of Systems Biology and Systems Medicine are included. To capture the wealth of information and contribute meaningfully to medical practice and biology research, Disease classification goes beyond a single spatial biologic level or static time assignment to include the interface of Disease process and organism response (Bechtel, 2017a; Green et al., 2017).

Integrated Omics: Tools, Advances, and Future Approaches

With the rapid adoption of high-throughput omic approaches to analyze biological samples such as genomics, transcriptomics, proteomics, and metabolomics, each analysis can generate tera- to peta-byte sized data files on a daily basis. These data file sizes, together with differences in nomenclature among these data types, make the integration of these multi-dimensional omics data into biologically meaningful context challenging. Variously named as integrated omics, multi-omics, poly-omics, trans-omics, pan-omics, or shortened to just 'omics', the challenges include differences in data cleaning, normalization, biomolecule identification, data dimensionality reduction, biological contextualization, statistical validation, data storage and handling, sharing, and data archiving. The ultimate goal is towards the holistic realization of a 'systems biology' understanding of the biological question in hand. Commonly used approaches in these efforts are currently limited by the 3 i's - integration, interpretation, and insights. Post integration, these very large datasets aim to yield unprecedented views of cellular systems at exquisite resolution for transformative insights into processes, events, and diseases through various computational and informatics frameworks. With the continued reduction in costs and processing time for sample analyses, and increasing types of omics datasets generated such as glycomics, lipidomics, microbiomics, and phenomics, an increasing number of scientists in this interdisciplinary domain of bioinformatics face these challenges. We discuss recent approaches, existing tools, and potential caveats in the integration of omics datasets for development of standardized analytical pipelines that could be adopted by the global omics research community.

Comprehensive metabolic profiling of chronic low-grade inflammation among generally healthy individuals

BACKGROUND

Inflammation occurs as an immediate protective response of the immune system to a harmful stimulus, whether locally confined or systemic. In contrast, a persisting, i.e., chronic, inflammatory state, even at a low-grade, is a well-known risk factor in the development of common diseases like diabetes or atherosclerosis. In clinical practice, laboratory markers like high-sensitivity C-reactive protein (hsCRP), white blood cell count (WBC), and fibrinogen, are used to reveal inflammatory processes. In order to gain a deeper insight regarding inflammation-related changes in metabolism, the present study assessed the metabolic patterns associated with alterations in inflammatory markers.

METHODS

Based on mass spectrometry and nuclear magnetic resonance spectroscopy we determined a comprehensive panel of 613 plasma and 587 urine metabolites among 925 apparently healthy individuals. Associations between inflammatory markers, namely hsCRP, WBC, and fibrinogen, and metabolite levels were tested by linear regression analyses controlling for common confounders. Additionally, we tested for a discriminative signature of an advanced inflammatory state using random forest analysis.

RESULTS

HsCRP, WBC, and fibrinogen were significantly associated with 71, 20, and 19 plasma and 22, 3, and 16 urine metabolites, respectively. Identified metabolites were related to the bradykinin system, involved in oxidative stress (e.g., glutamine or pipecolate) or linked to the urea cycle (e.g., ornithine or citrulline). In particular, urine 3'-sialyllactose was found as a novel metabolite related to inflammation. Prediction of an advanced inflammatory state based solely on 10 metabolites was well feasible (median AUC: 0.83).

CONCLUSIONS

Comprehensive metabolic profiling confirmed the far-reaching impact of inflammatory processes on human metabolism. The identified metabolites included not only those already described as immune-modulatory but also completely novel patterns. Moreover, the observed alterations provide molecular links to inflammation-associated diseases like diabetes or cardiovascular disorders.

Metabolic phenotyping for understanding the gut microbiome and host metabolic interplay

There is growing interest in the role of the gut microbiome in human health and disease. This unique complex ecosystem has been implicated in many health conditions, including intestinal disorders, inflammatory skin diseases and metabolic syndrome. However, there is still much to learn regarding its capacity to affect host health. Many gut microbiome research studies focus on compositional analysis to better understand the causal relationships between microbial communities and disease phenotypes. Yet, microbial diversity and complexity is such that community structure alone does not provide full understanding of microbial function. Metabolic phenotyping is an exciting field in systems biology that provides information on metabolic outputs taking place in the system at a given moment in time. These readouts provide information relating to by-products of endogenous metabolic pathways, exogenous signals arising from diet, drugs and other lifestyle and environmental stimuli, as well as products of microbe-host co-metabolism. Thus, better understanding of the gut microbiome and host metabolic interplay can be gleaned using such analytical approaches. In this review, we describe research findings focussed on gut microbiota-host interactions, for functional insights into the impact of microbiome composition on host health. We evaluate different analytical approaches for capturing metabolic activity and discuss analytical methodological advancements that have made a contribution to the field. This information will aid in developing novel approaches to improve host health in the future, and therapeutic modulation of the microbiome may soon augment conventional clinical strategies.

Metabolomic Profiling of Long-Term Weight Change: Role of Oxidative Stress and Urate Levels in Weight Gain

OBJECTIVE

To investigate the association between long-term weight change and blood metabolites.

METHODS

Change in BMI over 8.6 ± 3.79 years was assessed in 3,176 females from the TwinsUK cohort (age range: 18.3-79.6, baseline BMI: 25.11 ± 4.35) measured for 280 metabolites at follow-up. Statistically significant metabolites (adjusting for covariates) were included in a multivariable least absolute shrinkage and selection operator (LASSO) model. Findings were replicated in the Cooperative Health Research in the Region of Augsburg (KORA) study (n = 1,760; age range: 25-70, baseline BMI: 27.72 ± 4.53). The study examined whether the metabolites identified could prospectively predict weight change in KORA and in the Prostate, Lung, Colorectal, and Ovarian Cancer (PLCO) study (n = 471; age range: 55-74, baseline BMI: 27.24 ± 5.37).

RESULTS

Thirty metabolites were significantly associated with change in BMI per year in TwinsUK using Bonferroni correction. Four were independently associated with weight change in the multivariable LASSO model and replicated in KORA: namely, urate (meta-analysis β [95% CI] = 0.05 [0.040 to 0.063]; P = 1.37 × 10^-19 ), gamma-glutamyl valine (β [95% CI] = 0.06 [0.046 to 0.070]; P = 1.23 × 10^-20 ), butyrylcarnitine (β [95% CI] = 0.04 [0.028 to 0.051]; P = 6.72 × 10^-12 ), and 3-phenylpropionate (β [95% CI] = -0.03 [-0.041 to -0.019]; P = 9.8 × 10^-8 ), all involved in oxidative stress. Higher levels of urate at baseline were associated with weight gain in KORA and PLCO.

CONCLUSIONS

Metabolites linked to higher oxidative stress are associated with increased long-term weight gain.

Biological Age Predictors

The search for reliable indicators of biological age, rather than chronological age, has been ongoing for over three decades, and until recently, largely without success. Advances in the fields of molecular biology have increased the variety of potential candidate biomarkers that may be considered as biological age predictors. In this review, we summarize current state-of-the-art findings considering six potential types of biological age predictors: epigenetic clocks, telomere length, transcriptomic predictors, proteomic predictors, metabolomics-based predictors, and composite biomarker predictors. Promising developments consider multiple combinations of these various types of predictors, which may shed light on the aging process and provide further understanding of what contributes to healthy aging. Thus far, the most promising, new biological age predictor is the epigenetic clock; however its true value as a biomarker of aging requires longitudinal confirmation.

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Telomere length is the most well studied biological age predictor, but many new predictors are emerging.

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The epigenetic clock is currently the best biological age predictor, as it correlates well with age and predicts mortality.

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The various biological age predictors tend to reflect different aspects of the aging process.