Systems Epidemiology: What's in a Name?

A network analysis of nutritional markers and maternal perinatal mental health in the French EDEN cohort

BACKGROUND

Perinatal maternal depression and anxiety are associated with adverse maternal outcomes, and nutrition may play an important role in their emergence. Previous research shows that certain micro and macronutrients found in different dietary patterns may associate with perinatal mood disorders. This study aims to explore relationships between nutrition during pregnancy and perinatal maternal depression and anxiety symptoms using network analyses.

METHODS

Using data from the French EDEN mother-child cohort, the sample consisted of 1438 women with available mental health outcomes (CES-D, STAI and EPDS) and nutritional markers collected from food frequency questionnaires. Four networks were constructed to explore the relationships between prenatal nutrient status, dietary patterns, and perinatal mental health, while accounting for important confounders.

RESULTS

The Healthy dietary pattern was associated with the presence of vital micronutrients, while the Western dietary pattern was consistently associated with poorer intake of specific micronutrients and contained an excess of certain macronutrients. Western dietary pattern and symptoms of postnatal depression were connected by a positive edge in both the macronutrient and micronutrient networks. Lower education levels were associated with higher Western dietary pattern scores, from which a positive edge linked to postnatal depression symptoms in both models.

CONCLUSIONS

A Western dietary pattern was associated with increased symptoms of postnatal depression in our adjusted network models; The Healthy dietary pattern was associated with essential micronutrients but not with symptoms of depression or anxiety. Perinatal mental health might be impacted by specific dietary patterns in the context of psychosocial and physical stress associated with pregnancy.

Cohort profile: The Clinical and Multi-omic (CAMO) cohort, part of the Norwegian Women and Cancer (NOWAC) study

INTRODUCTION

Breast cancer is the most common cancer worldwide and the leading cause of cancer related deaths among women. The high incidence and mortality of breast cancer calls for improved prevention, diagnostics, and treatment, including identification of new prognostic and predictive biomarkers for use in precision medicine.

MATERIAL AND METHODS

With the aim of compiling a cohort amenable to integrative study designs, we collected detailed epidemiological and clinical data, blood samples, and tumor tissue from a subset of participants from the prospective, population-based Norwegian Women and Cancer (NOWAC) study. These study participants were diagnosed with invasive breast cancer in North Norway before 2013 according to the Cancer Registry of Norway and constitute the Clinical and Multi-omic (CAMO) cohort. Prospectively collected questionnaire data on lifestyle and reproductive factors and blood samples were extracted from the NOWAC study, clinical and histopathological data were manually curated from medical records, and archived tumor tissue collected.

RESULTS

The lifestyle and reproductive characteristics of the study participants in the CAMO cohort (n = 388) were largely similar to those of the breast cancer patients in NOWAC (n = 10 356). The majority of the cancers in the CAMO cohort were tumor grade 2 and of the luminal A subtype. Approx. 80% were estrogen receptor positive, 13% were HER2 positive, and 12% were triple negative breast cancers. Lymph node metastases were present in 31% at diagnosis. The epidemiological dataset in the CAMO cohort is complemented by mRNA, miRNA, and metabolomics analyses in plasma, as well as miRNA profiling in tumor tissue. Additionally, histological analyses at the level of proteins and miRNAs in tumor tissue are currently ongoing.

CONCLUSION

The CAMO cohort provides data suitable for epidemiological, clinical, molecular, and multi-omics investigations, thereby enabling a systems epidemiology approach to translational breast cancer research.

The Global Pregnancy Collaboration (CoLab) symposium on short- and long-term outcomes in offspring whose mothers had preeclampsia: A scoping review of clinical evidence

Preeclampsia is a maternal syndrome characterized by the new onset of hypertension after 20 weeks of gestation associated with multisystemic complications leading to high maternal and fetal/neonatal morbidity and mortality. However, sequelae of preeclampsia may extend years after pregnancy in both mothers and their children. In addition to the long-term adverse cardiovascular effects of preeclampsia in the mother, observational studies have reported elevated risk of cardiovascular, metabolic, cerebral and cognitive complications in children born from women with preeclampsia. Less clear is whether the association between maternal preeclampsia and offspring sequelae are causal, or to what degree the associations might be driven by fetal factors including impaired growth and the health of its placenta. Our discussion of these complexities in the 2018 Global Pregnancy Collaboration annual meeting prompted us to write this review. We aimed to summarize the evidence of an association between maternal preeclampsia and neurobehavioral developmental disorders in offspring in hopes of generating greater research interest in this important topic.

Facilitating cancer systems epidemiology research

Systems epidemiology offers a more comprehensive and holistic approach to studies of cancer in populations by considering high dimensionality measures from multiple domains, assessing the inter-relationships among risk factors, and considering changes over time. These approaches offer a framework to account for the complexity of cancer and contribute to a broader understanding of the disease. Therefore, NCI sponsored a workshop in February 2019 to facilitate discussion about the opportunities and challenges of the application of systems epidemiology approaches for cancer research. Eight key themes emerged from the discussion: transdisciplinary collaboration and a problem-based approach; methods and modeling considerations; interpretation, validation, and evaluation of models; data needs and opportunities; sharing of data and models; enhanced training practices; dissemination of systems models; and building a systems epidemiology community. This manuscript summarizes these themes, highlights opportunities for cancer systems epidemiology research, outlines ways to foster this research area, and introduces a collection of papers, "Cancer System Epidemiology Insights and Future Opportunities" that highlight findings based on systems epidemiology approaches.

Network Analysis in Systems Epidemiology

Traditional epidemiological studies have identified a number of risk factors for various diseases using regression-based methods that examine the association between an exposure and an outcome (i.e., one-to-one correspondences). One of the major limitations of this approach is the "black-box" aspect of the analysis, in the sense that this approach cannot fully explain complex relationships such as biological pathways. With high-throughput data in current epidemiology, comprehensive analyses are needed. The network approach can help to integrate multi-omics data, visualize their interactions or relationships, and make inferences in the context of biological mechanisms. This review aims to introduce network analysis for systems epidemiology, its procedures, and how to interpret its findings.

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.

Reflecting on 20 years of breast cancer modeling in CISNET: Recommendations for future cancer systems modeling efforts

Since 2000, the National Cancer Institute’s Cancer Intervention and Surveillance Modeling Network (CISNET) modeling teams have developed and applied microsimulation and statistical models of breast cancer. Here, we illustrate the use of collaborative breast cancer multilevel systems modeling in CISNET to demonstrate the flexibility of systems modeling to address important clinical and policy-relevant questions. Challenges and opportunities of future systems modeling are also summarized. The 6 CISNET breast cancer models embody the key features of systems modeling by incorporating numerous data sources and reflecting tumor, person, and health system factors that change over time and interact to affect the burden of breast cancer. Multidisciplinary modeling teams have explored alternative representations of breast cancer to reveal insights into breast cancer natural history, including the role of overdiagnosis and race differences in tumor characteristics. The models have been used to compare strategies for improving the balance of benefits and harms of breast cancer screening based on personal risk factors, including age, breast density, polygenic risk, and history of Down syndrome or a history of childhood cancer. The models have also provided evidence to support the delivery of care by simulating outcomes following clinical decisions about breast cancer treatment and estimating the relative impact of screening and treatment on the United States population. The insights provided by the CISNET breast cancer multilevel modeling efforts have informed policy and clinical guidelines. The 20 years of CISNET modeling experience has highlighted opportunities and challenges to expanding the impact of systems modeling. Moving forward, CISNET research will continue to use systems modeling to address cancer control issues, including modeling structural inequities affecting racial disparities in the burden of breast cancer. Future work will also leverage the lessons from team science, expand resource sharing, and foster the careers of early stage modeling scientists to ensure the sustainability of these efforts.

Since 2000, our research teams have used computer models of breast cancer to address important clinical and policy-relevant questions as part of the National Cancer Institute’s Cancer Intervention and Surveillance Modeling Network (CISNET). Our 6 CISNET breast cancer models embody the key features of systems modeling by incorporating numerous data sources and reflecting tumor, person, and health system factors that change over time and interact to represent the burden of breast cancer. We have used our models to investigate questions related to breast cancer biology, compare strategies to improve the balance of benefits and harms of screening mammography, and support insights into the delivery of care by modeling outcomes following clinical decisions about breast cancer treatment. Moving forward, our research will continue to use systems modeling to address issues related to reducing the burden of breast cancer including modeling structural inequities affecting racial disparities. Our future work will also leverage lessons from engaging multidisciplinary scientific teams, expand efforts to share modeling resources with other researchers, and foster the careers of early stage modeling scientists to ensure the sustainability of these efforts.

Systems epidemiology and cancer: A review of the National Institutes of Health extramural grant portfolio 2013-2018

OBJECTIVES

Systems epidemiology approaches may lead to a better understanding of the complex and dynamic multi-level constellation of contributors to cancer risk and outcomes and help target interventions. This grant portfolio analysis aimed to describe the National Institutes of Health (NIH) and the National Cancer Institute (NCI) investments in systems epidemiology and to identify gaps in the cancer systems epidemiology portfolio.

METHODS

The analysis examined grants funded (2013-2018) through seven NIH systems science Funding Opportunity Announcements (FOAs) as well as cancer-specific systems epidemiology grants funded by NCI during that same time. Study characteristics were extracted from the grant abstracts and specific aims and coded.

RESULTS

Of the 137 grants awarded under the NIH FOAs, 52 (38%) included systems epidemiology. Only five (4%) were focused on cancer systems epidemiology. The NCI-wide search (N = 453 grants) identified 35 grants (8%) that included cancer systems epidemiology in their specific aims. Most of these grants examined epidemiology and surveillance-based questions (60%); fewer addressed clinical care or clinical trials (37%). Fifty-four percent looked at multiple scales within the individual (e.g., cell, tissue, organ), 49% looked beyond the individual (e.g., individual, community, population), and few (9%) included both. Across all grants examined, the systems epidemiology grants primarily focused on discovery or prediction, rather than on impacts of intervention or policy.

CONCLUSIONS

The most notable finding was that grants focused on cancer versus other diseases reflected a small percentage of the portfolio, highlighting the need to encourage more cancer systems epidemiology research. Opportunities include encouraging more multiscale research and continuing the support for broad examination of domains in these studies. Finally, the nascent discipline of systems epidemiology could benefit from the creation of standard terminology and definitions to guide future progress.

The Epidemiology of Plant Virus Disease: Towards a New Synthesis

Epidemiology is the science of how disease develops in populations, with applications in human, animal and plant diseases. For plant diseases, epidemiology has developed as a quantitative science with the aims of describing, understanding and predicting epidemics, and intervening to mitigate their consequences in plant populations. Although the central focus of epidemiology is at the population level, it is often necessary to recognise the system hierarchies present by scaling down to the individual plant/cellular level and scaling up to the community/landscape level. This is particularly important for diseases caused by plant viruses, which in most cases are transmitted by arthropod vectors. This leads to range of virus-plant, virus-vector and vector-plant interactions giving a distinctive character to plant virus epidemiology (whilst recognising that some fungal, oomycete and bacterial pathogens are also vector-borne). These interactions have epidemiological, ecological and evolutionary consequences with implications for agronomic practices, pest and disease management, host resistance deployment, and the health of wild plant communities. Over the last two decades, there have been attempts to bring together these differing standpoints into a new synthesis, although this is more apparent for evolutionary and ecological approaches, perhaps reflecting the greater emphasis on shorter often annual time scales in epidemiological studies. It is argued here that incorporating an epidemiological perspective, specifically quantitative, into this developing synthesis will lead to new directions in plant virus research and disease management. This synthesis can serve to further consolidate and transform epidemiology as a key element in plant virus research.

A novel path-specific effect statistic for identifying the differential specific paths in systems epidemiology

BACKGROUND

Biological pathways play an important role in the occurrence, development and recovery of complex diseases, such as cancers, which are multifactorial complex diseases that are generally caused by mutation of multiple genes or dysregulation of pathways.

RESULTS

We propose a path-specific effect statistic (PSE) to detect the differential specific paths under two conditions (e.g. case VS. control groups, exposure Vs. nonexposure groups). In observational studies, the path-specific effect can be obtained by separately calculating the average causal effect of each directed edge through adjusting for the parent nodes of nodes in the specific path and multiplying them under each condition. Theoretical proofs and a series of simulations are conducted to validate the path-specific effect statistic. Applications are also performed to evaluate its practical performances. A series of simulation studies show that the Type I error rates of PSE with Permutation tests are more stable at the nominal level 0.05 and can accurately detect the differential specific paths when comparing with other methods. Specifically, the power reveals an increasing trends with the enlargement of path-specific effects and its effect differences under two conditions. Besides, the power of PSE is robust to the variation of parent or child node of the nodes on specific paths. Application to real data of Glioblastoma Multiforme (GBM), we successfully identified 14 positive specific pathways in mTOR pathway contributing to survival time of patients with GBM. All codes for automatic searching specific paths linking two continuous variables and adjusting set as well as PSE statistic can be found in supplementary materials.  CONCLUSION: The proposed PSE statistic can accurately detect the differential specific pathways contributing to complex disease and thus potentially provides new insights and ways to unlock the black box of disease mechanisms.

The Population Health OutcomEs aNd Information EXchange (PHOENIX) Program - A Transformative Approach to Reduce the Burden of Chronic Disease

This concept article introduces a transformative vision to reduce the population burden of chronic disease by focusing on data integration, analytics, implementation and community engagement. Known as PHOENIX (The Population Health OutcomEs aNd Information EXchange), the approach leverages a state level health information exchange and multiple other resources to facilitate the integration of clinical and social determinants of health data with a goal of achieving true population health monitoring and management. After reviewing historical context, we describe how multilevel and multimodal data can be used to facilitate core public health services, before discussing the controversies and challenges that lie ahead.

Metabolomics in genetic testing

Metabolomics is an intriguing field of study providing a new readout of the biochemical activities taking place at the moment of sampling within a subject's biofluid or tissue. Metabolite concentrations are influenced by several factors including disease, environment, drugs, diet and, importantly, genetics. Metabolomics signatures, which describe a subject's phenotype, are useful for disease diagnosis and prognosis, as well as for predicting and monitoring the effectiveness of treatments. Metabolomics is conventionally divided into targeted (i.e., the quantitative analysis of a predetermined group of metabolites) and untargeted studies (i.e., analysis of the complete set of small-molecule metabolites contained in a biofluid without a pre-imposed metabolites-selection). Both approaches have demonstrated high value in the investigation and understanding of several monogenic and multigenic conditions. Due to low costs per sample and relatively short analysis times, metabolomics can be a useful and robust complement to genetic sequencing.

Lessons from Outside and Within: Exploring Advancements in Methodology for Naturopathic Medicine Clinical Research

INTRODUCTION

Naturopathy is a mixture of both traditional and complementary medicine. It incorporates a broad set of health care practices that may or may not be traditional to that country or conventional medicine and are not fully integrated into the dominant health care system. Research required to evaluate or substantiate naturopathic medicine may not fall under the testing of randomized clinical trials, which opens up discussions on what is the best practice for research in naturopathic medicine.

DISCUSSION

Not only do advances in health research methodology offer important opportunities to progress naturopathic research, there are also areas where the unique characteristics of naturopathic philosophy and practice can impact other areas of health research. Some of the new advances in health research methodology involve whole-system research, pragmatic trials, template for intervention description and replication protocols for complex interventions, patient-centered care models, and the pragmatic-explanatory continuum indicator summary tool for designing pragmatic trials. Discussion and critique of these health-related methodologies shows that these research methods are more suited for the philosophy and treatment options that naturopathy is based on.

CONCLUSIONS

Successful implementation of naturopathic research methodologies, and translation and dissemination of research will require a substantial paradigm shift in which naturopathic practitioners adopt a greater level of responsibility for developing an evidence base for naturopathic medicine.

Effects of environmental and genetic risk factors for salt sensitivity on blood pressure in northern China: the systemic epidemiology of salt sensitivity (EpiSS) cohort study

PURPOSE

The systemic epidemiology of salt sensitivity (EpiSS) study aims to combine molecular biology, epidemiology and bioinformatics methods to discover the potential causes of salt sensitivity of blood pressure (SSBP) using single-nucleotide polymorphisms in the genome and non-coding RNAs in the transcriptome to uncover both the genetic and environmental factors of SSBP.

PARTICIPANTS

Between July 2014 and July 2016, we enrolled adults from 11 study centres in Beijing and Liaoning Province; participants were of the Han population and were 35-70 years of age. We collected blood samples, spot urine samples and 24-hour urine samples, in addition to baseline data on demographics, health-related lifestyle factors, chronic diseases, family history of illness and anthropometric information through face-to-face interviews using a standardised questionnaire. EpiSS uses the modified Sullivan's acute oral saline load and diuresis shrinkage test (MSAOSL-DST) to evaluate the effects of salt on blood pressure.

FINDINGS TO DATE

In total, 2163 participants were included in the EpiSS, of which 2144 participants completed the questionnaire, 2120 (98.0%) completed the MSAOSL-DST and 2083 (96.3%) provided a 24-hour urine sample. A total of 2057 participants (1501 women and 556 men) completed all the steps of the investigation and were included in the analysis. Among them, 583 (28.3%) subjects were classified as having salt sensitivity of blood pressure, and 1061 (51.6%) had hypertension.

FUTURE PLANS

The next step of our study is to evaluate the incidence of cardiovascular disease in the participants. Biennial follow-up, including face-to-face questionnaire surveys, laboratory measurements of blood, urinary creatinine, glomerular filtration rate and anthropometric measurements, will occur two additional times. DNA and RNA will be collected for subsequent genetic biomarker studies. We plan on screening the salt-sensitive-related gene loci and non-coding RNAs based on relative environmental risk factors.

TRIAL REGISTRATION NUMBER

ChiCTR-EOC-16009980; Pre-results.

Applying systems biology to biomedical research and health care: a précising definition of systems medicine

BACKGROUND

Systems medicine has become a key word in biomedical research. Although it is often referred to as P4-(predictive, preventive, personalized and participatory)-medicine, it still lacks a clear definition and is open to interpretation. This conceptual lack of clarity complicates the scientific and public discourse on chances, risks and limits of Systems Medicine and may lead to unfounded hopes. Against this background, our goal was to develop a sufficiently precise and widely acceptable definition of Systems Medicine.

METHODS

In a first step, PubMed was searched using the keyword "systems medicine". A data extraction tabloid was developed putting forward a means/ends-division. Full-texts of articles containing Systems Medicine in title or abstract were screened for definitions. Definitions were extracted; their semantic elements were assigned as either means or ends. To reduce complexity of the resulting list, summary categories were developed inductively. In a second step, we applied six criteria for adequate definitions (necessity, non-circularity, non-redundancy, consistency, non-vagueness, and coherence) to these categories to derive a so-called précising definition of Systems Medicine.

RESULTS

We identified 185 articles containing the term Systems Medicine in title or abstract. 67 contained at least one definition of Systems Medicine. In 98 definitions, we found 114 means and 132 ends. From these we derived the précising definition: Systems Medicine is an approach seeking to improve medical research (i.e. the understanding of complex processes occurring in diseases, pathologies and health states as well as innovative approaches to drug discovery) and health care (i.e. prevention, prediction, diagnosis and treatment) through stratification by means of Systems Biology (i.e. data integration, modeling, experimentation and bioinformatics). Our study also revealed the visionary character of Systems Medicine.

CONCLUSIONS

Our insights, on the one hand, allow for a realistic identification of actual ethical as well as legal issues arising in the context of Systems Medicine and, in consequence, for a realistic debate of questions concerning its matter and (future) handling. On the other hand, they help avoiding unfounded hopes and unrealistic expectations. This especially holds for goals like improving patient participation which are intensely debated in the context of Systems Medicine, however not implied in the concept.

Meta-analysis for genome-wide association studies using case-control design: application and practice

This review aimed to arrange the process of a systematic review of genome-wide association studies in order to practice and apply a genome-wide meta-analysis (GWMA). The process has a series of five steps: searching and selection, extraction of related information, evaluation of validity, meta-analysis by type of genetic model, and evaluation of heterogeneity. In contrast to intervention meta-analyses, GWMA has to evaluate the Hardy-Weinberg equilibrium (HWE) in the third step and conduct meta-analyses by five potential genetic models, including dominant, recessive, homozygote contrast, heterozygote contrast, and allelic contrast in the fourth step. The 'genhwcci' and 'metan' commands of STATA software evaluate the HWE and calculate a summary effect size, respectively. A meta-regression using the 'metareg' command of STATA should be conducted to evaluate related factors of heterogeneities.

Gene Sequence Analyses of the Healthy Oral Microbiome in Humans and Companion Animals

It has long been accepted that certain oral bacterial species are responsible for the development of periodontal disease. However, the focus of microbial and immunological research is shifting from studying the organisms associated with disease to examining the indigenous microbial inhabitants that are present in health. Microbiome refers to the aggregate genetic material of all microorganisms living in, or on, a defined habitat. Recent developments in gene sequence analysis have enabled detection and identification of bacteria from polymicrobial samples, including subgingival plaque. Diversity surveys utilizing this technology have demonstrated that bacterial culture techniques have vastly underestimated the richness and diversity of microorganisms in vivo, since only certain bacteria grow in vitro. Surveys using gene sequence analysis have demonstrated that the healthy oral microbiome is composed of an unexpectedly high number of diverse species, including putative pathogens. These findings support the view that coevolution microorganisms and macroscopic hosts has occurred in which certain microorganisms have adapted to survive in the oral cavity and host immune tolerance has allowed the establishment of a symbiotic relationship in which both parties receive benefits (mutualism). This review describes gene sequence analysis as an increasingly common, culture-independent tool for detecting bacteria in vivo and describes the results of recent oral microbiome diversity surveys of clinically healthy humans, dogs, and cats. Six bacterial phyla consistently dominated the healthy oral microbiome of all 3 host species. Previous hypotheses on etiology of periodontitis are reviewed in light of new scientific findings. Finally, the consideration that clinically relevant periodontal disease occurs when immune tolerance of the symbiotic oral microbiome is altered to a proinflammatory response will be discussed.