scholarly journals Identification of host-pathogen-disease relationships using a scalable Multiplex Serology platform in UK Biobank

2019 ◽  
Author(s):  
Alexander J Mentzer ◽  
Nicole Brenner ◽  
Naomi Allen ◽  
Thomas J Littlejohns ◽  
Amanda Y Chong ◽  
...  
Keyword(s):  
Large Scale ◽  
Disease Risk ◽  
Infectious Agent ◽  
Epidemiological Studies ◽  
Antibody Responses ◽  
Infectious Agents ◽  
Uk Biobank ◽  
Disease Outcomes ◽  
Host Pathogen ◽  
Multiplex Serology

AbstractBackgroundCertain infectious agents are recognised causes of cancer and potentially other chronic diseases. Identifying associations and understanding pathological mechanisms involving infectious agents and subsequent chronic disease risk will be possible through measuring exposure to multiple infectious agents in large-scale prospective cohorts such as UK Biobank.MethodsFollowing expert consensus we designed a Multiplex Serology platform capable of simultaneously measuring quantitative antibody responses against 45 antigens from 20 infectious agents implicated in non-communicable diseases, including human herpes, hepatitis, polyoma, papilloma, and retroviruses, as well as Chlamydia trachomatis, Helicobacter pylori and Toxoplasma gondii. This panel was assayed in a random subset of UK Biobank participants (n=9,695) to test associations between infectious agents and recognised demographic and genetic risk factors and disease outcomes.FindingsSeroprevalence estimates for each infectious agent were consistent with those expected from the literature. The data confirmed epidemiological associations of infectious agent antibody responses with sociodemographic characteristics (e.g. lifetime sexual partners with C, trachomatis; P=1·8×10−149), genetic variants (e.g. rs6927022 with Epstein-Barr virus (EBV) EBNA1 antibodies, P=9·5×10−91) and disease outcomes including human papillomavirus-16 seropositivity and cervical intraepithelial neoplasia (odds ratio 2·28, 95% confidence interval 1·38-3·63), and quantitative EBV viral capsid antigen responses and multiple sclerosis through genetic correlation (MHC rG=0·30, P=0·01).InterpretationThis dataset, intended as a pilot study to demonstrate applicability of Multiplex Serology in epidemiological studies, is itself one of the largest studies to date covering diverse infectious agents in a prospective UK cohort including those traditionally under-represented in population cohorts such as human immunodeficiency virus-1 and C. trachomatis. Our results emphasise the validity of our Multiplex Serology approach in large-scale epidemiological studies opening up opportunities for improving our understanding of host-pathogen-disease relationships. These data are available to researchers interested in examining the relationship between infectious agents and human health.

scholarly journals INFECTIONS AND FOLLICULAR LYMPHOMA: IS THERE A LINK?

2017 ◽  
Vol 9 (1) ◽  
pp. e2017035
Author(s):  
Francesco Zallio ◽  
Giulia Limberti ◽  
Marco Ladetto
Keyword(s):  
Follicular Lymphoma ◽  
B Cell ◽  
Bacterial Infections ◽  
Classical Model ◽  
Infectious Agent ◽  
Epidemiological Studies ◽  
Gastric Malt Lymphoma ◽  
Infectious Agents ◽  
Non Hodgkin Lymphoma ◽  
Treatment And Prevention

Several infectious agents appear to provide a proliferative signal -- “antigen-drive” – that  could be implicated in the pathogenesis of various type of Non-Hodgkin Lymphoma (NHL). A classical model of infection-driven lymphoprolipherative disorder is Helicobacter pylori-induced gastric MALT lymphoma, where antibiotic therapy allows eradication of both the infectious agent and the clonal B-cell expansion;  following the footsteps of these example, several retrospective studies have found a correlation with other pathogens and B-cell Lymphomas, adding new important informations about pathogenesis and laying the groundwork for chemotherapy-free treatments.Although no clear association with infectious agents has yet been identified for Follicular Lymphoma (FL), a growing number of biological and clinical observations suggests that interaction with physiological and pathological microbial populations might play a role also in this subtype of lymphoma: in the last years epidemiological studies investigating the association of known risk factors and FL found a potential correlation with viral or bacterial infections; moreover recent findings about the stimulation of FL clones support the importance of microbial exposure to lymphomagenesis and disease progression.In the following review we make an attempt to find tangible evidences in favor of a role of either physiological and pathological exogenous microbial species in the pathogenesis of FL, and try to integrate the findings coming from epidemiological, biological and interventional studies to define future  novel treatment and prevention strategies for FL.

scholarly journals Analysis of Serological Biomarkers of SARS-CoV-2 Infection in Convalescent Samples From Severe, Moderate and Mild COVID-19 Cases

2021 ◽  
Vol 12 ◽  
Author(s):  
Javier Castillo-Olivares ◽  
David A. Wells ◽  
Matteo Ferrari ◽  
Andrew C. Y. Chan ◽  
Peter Smith ◽  
...  
Keyword(s):  
Large Scale ◽  
World Health Organisation ◽  
Epidemiological Studies ◽  
Antibody Responses ◽  
Clinical Severity ◽  
World Health ◽  
Binding Assays ◽  
Neutralising Antibodies ◽  
Antibody Assays ◽  
Binding Antibody

Precision monitoring of antibody responses during the COVID-19 pandemic is increasingly important during large scale vaccine rollout and rise in prevalence of Severe Acute Respiratory Syndrome-related Coronavirus-2 (SARS-CoV-2) variants of concern (VOC). Equally important is defining Correlates of Protection (CoP) for SARS-CoV-2 infection and COVID-19 disease. Data from epidemiological studies and vaccine trials identified virus neutralising antibodies (Nab) and SARS-CoV-2 antigen-specific (notably RBD and S) binding antibodies as candidate CoP. In this study, we used the World Health Organisation (WHO) international standard to benchmark neutralising antibody responses and a large panel of binding antibody assays to compare convalescent sera obtained from: a) COVID-19 patients; b) SARS-CoV-2 seropositive healthcare workers (HCW) and c) seronegative HCW. The ultimate aim of this study is to identify biomarkers of humoral immunity that could be used to differentiate severe from mild or asymptomatic SARS-CoV-2 infections. Some of these biomarkers could be used to define CoP in further serological studies using samples from vaccination breakthrough and/or re-infection cases. Whenever suitable, the antibody levels of the samples studied were expressed in International Units (IU) for virus neutralisation assays or in Binding Antibody Units (BAU) for ELISA tests. In this work we used commercial and non-commercial antibody binding assays; a lateral flow test for detection of SARS-CoV-2-specific IgG/IgM; a high throughput multiplexed particle flow cytometry assay for SARS-CoV-2 Spike (S), Nucleocapsid (N) and Receptor Binding Domain (RBD) proteins); a multiplex antigen semi-automated immuno-blotting assay measuring IgM, IgA and IgG; a pseudotyped microneutralisation test (pMN) and an electroporation-dependent neutralisation assay (EDNA). Our results indicate that overall, severe COVID-19 patients showed statistically significantly higher levels of SARS-CoV-2-specific neutralising antibodies (average 1029 IU/ml) than those observed in seropositive HCW with mild or asymptomatic infections (379 IU/ml) and that clinical severity scoring, based on WHO guidelines was tightly correlated with neutralisation and RBD/S antibodies. In addition, there was a positive correlation between severity, N-antibody assays and intracellular virus neutralisation.

scholarly journals New perspectives on bioactivity of olive oil: evidence from animal models, human interventions and the use of urinary proteomic biomarkers

2015 ◽  
Vol 74 (3) ◽  
pp. 268-281 ◽  
Author(s):  
S. Silva ◽  
E. Combet ◽  
M. E. Figueira ◽  
T. Koeck ◽  
W. Mullen ◽  
...  
Keyword(s):  
Olive Oil ◽  
Large Scale ◽  
Disease Risk ◽  
Primary Source ◽  
Epidemiological Studies ◽  
Health Claims ◽  
Model Systems ◽  
Human Interventions ◽  
Novel Biomarkers ◽  
The Impact

Olive oil (OO) is the primary source of fat in the Mediterranean diet and has been associated with longevity and a lower incidence of chronic diseases, particularly CHD. Cardioprotective effects of OO consumption have been widely related with improved lipoprotein profile, endothelial function and inflammation, linked to health claims of oleic acid and phenolic content of OO. With CVD being a leading cause of death worldwide, a review of the potential mechanisms underpinning the impact of OO in the prevention of disease is warranted. The current body of evidence relies on mechanistic studies involving animal and cell-based models, epidemiological studies of OO intake and risk factor, small- and large-scale human interventions, and the emerging use of novel biomarker techniques associated with disease risk. Although model systems are important for mechanistic research nutrition, methodologies and experimental designs with strong translational value are still lacking. The present review critically appraises the available evidence to date, with particular focus on emerging novel biomarkers for disease risk assessment. New perspectives on OO research are outlined, especially those with scope to clarify key mechanisms by which OO consumption exerts health benefits. The use of urinary proteomic biomarkers, as highly specific disease biomarkers, is highlighted towards a higher translational approach involving OO in nutritional recommendations.

Ultra-processed/Ultra-formulated foods: association with chronic disease risk

2020 ◽  
Vol 30 (Supplement_5) ◽  
Author(s):  
M Touvier
Keyword(s):  
Large Scale ◽  
Disease Risk ◽  
Heart Diseases ◽  
Epidemiological Studies ◽  
Processed Foods ◽  
Breast Cancers ◽  
Dietary Intakes ◽  
Research Perspectives

Abstract During the past decades, diets have shifted towards an important increase in the degree of food processing and formulation. “Ultra-processed foods” (UPF) now represent more than 50% of energy intakes in several Western countries. In the very last years, an impressive accumulation of evidence from large-scale epidemiological studies linked regular UPF consumption to diverse adverse health outcomes. In this framework, our team of conducted pioneer studies within the prospective e-cohort NutriNet-Santé (n = 170 000) launched in 2009 in France. Dietary intakes were collected using repeated and validated 24-hour dietary records, covering >3,500 food items, which have been categorized using the NOVA classification according to their degree of processing. These analyses highlighted robust significant associations between the consumption of UPF and increased risks of overall and breast cancers, cardiovascular, cerebrovascular, and coronary heart diseases, mortality, type 2-diabetes, overweight, obesity and weight gain, depressive symptoms, and gastro-intestinal disorders. Research perspectives now consist in elucidating the potential mechanisms that underlie these associations. Our team is launching an Europe-funded project built as a combination of epidemiological studies and in-vitro/in-vivo experiments, in order to shed light on individual exposure to food additive 'cocktails' in relation to human health. Meanwhile and even if further studies are needed to better understand the relative contributions of these factors, public health authorities in several countries have recently started to promote unprocessed or minimally processed foods and to recommend limiting the consumption of ultra-processed foods.

scholarly journals Population structure and genetic diversity of non-O157 Shiga toxin-producing Escherichia coli (STEC) clinical isolates from Michigan

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Heather M. Blankenship ◽  
Rebekah E. Mosci ◽  
Stephen Dietrich ◽  
Elizabeth Burgess ◽  
Jason Wholehan ◽  
...  
Keyword(s):  
Genetic Variation ◽  
Disease Risk ◽  
Phylogenetic Analyses ◽  
Parallel Evolution ◽  
Virulence Gene ◽  
Epidemiological Data ◽  
Epidemiological Studies ◽  
Gene Profiles ◽  
Disease Outcomes ◽  
Sequence Types

AbstractNon-O157 STEC are increasingly linked to foodborne infections, yet little is known about the diversity and molecular epidemiology across locations. Herein, we used whole genome sequencing to examine genetic variation in 894 isolates collected from Michigan patients between 2001 and 2018. In all, 67 serotypes representing 69 multilocus sequence types were identified. Serotype diversity increased from an average of four (2001–2006) to 17 (2008–2018) serotypes per year. The top six serogroups reported nationally caused > 60% of infections in 16 of the 18 years; serogroups O111 and O45 were associated with hospitalization as were age ≥ 65 years, diarrhea with blood and female sex. Phylogenetic analyses of seven multilocus sequence typing (MLST) loci identified three clades as well as evidence of parallel evolution and recombination. Most (95.5%) isolates belonged to one clade, which could be further differentiated into seven subclades comprising isolates with varying virulence gene profiles and serotypes. No association was observed between specific clades and the epidemiological data, suggesting that serogroup- and serotype-specific associations are more important predictors of disease outcomes than lineages defined by MLST. Molecular epidemiological studies of non-O157 STEC are important to enhance understanding of circulating strain distributions and traits, genetic variation, and factors that may impact disease risk and severity.

scholarly journals Causal attribution fractions for epidemiological studies, applied to a UK Biobank study of smoking and BMI

2021 ◽  
Author(s):  
Anthony Webster
Keyword(s):  
Risk Factors ◽  
Proportional Hazards ◽  
Disease Risk ◽  
Causal Attribution ◽  
Disease Incidence ◽  
World Health Organisation ◽  
Epidemiological Studies ◽  
World Health ◽  
Proportional Hazard ◽  
Uk Biobank

Epidemiological studies often use proportional hazard models to estimate associations between potential risk factors and disease risk. It is emphasised that when the "backdoor criteria" from causal-inference applies, if diseases are sufficiently rare, then the proportional hazard model can be used to estimate causal associations. When the "frontdoor criteria" applies (allowing causal estimates with unmeasured confounders), similar estimates are found to mediation analyses with measured confounders. Reasons for this are discussed. An attribution fraction is constructed using the average causal effects (ACE) of exposures on the population, and simple methods for its evaluation are suggested. It differs from the attribution fraction used by the World Health Organisation (WHO), except for specific circumstances where the latter can agree or provide a bound. A counterfactual argument determines an individual's attribution fraction Af in terms of proportional hazard estimates, as Af = 1 − 1/R, where R is an individual's relative risk. Causally meaningful attribution fractions cannot be constructed for all known risk factors or confounders, but there are important cases where they can. As an example, systematic proportional hazards studies with UK Biobank data estimate the attribution fractions of smoking and BMI for 226 diseases. The attribution of risk is characterised in terms of disease chapters from the International Classification of Diseases (ICD-10), and the diseases most strongly attributed to smoking and BMI are identified. The result is a quantitative characterisation of the causal influence of smoking and BMI on the landscape of disease incidence in the UK Biobank population.

scholarly journals Collider bias undermines our understanding of COVID-19 disease risk and severity

2020 ◽  
Vol 11 (1) ◽  
Author(s):  
Gareth J. Griffith ◽  
Tim T. Morris ◽  
Matthew J. Tudball ◽  
Annie Herbert ◽  
Giulia Mancano ◽  
...  
Keyword(s):  
Observational Studies ◽  
Disease Risk ◽  
Observational Evidence ◽  
Design Stage ◽  
Sampling Strategies ◽  
Uk Biobank ◽  
Active Infection ◽  
Disease Outcomes ◽  
Representative Samples ◽  
Collider Bias

AbstractNumerous observational studies have attempted to identify risk factors for infection with SARS-CoV-2 and COVID-19 disease outcomes. Studies have used datasets sampled from patients admitted to hospital, people tested for active infection, or people who volunteered to participate. Here, we highlight the challenge of interpreting observational evidence from such non-representative samples. Collider bias can induce associations between two or more variables which affect the likelihood of an individual being sampled, distorting associations between these variables in the sample. Analysing UK Biobank data, compared to the wider cohort the participants tested for COVID-19 were highly selected for a range of genetic, behavioural, cardiovascular, demographic, and anthropometric traits. We discuss the mechanisms inducing these problems, and approaches that could help mitigate them. While collider bias should be explored in existing studies, the optimal way to mitigate the problem is to use appropriate sampling strategies at the study design stage.

Sign in / Sign up

Export Citation Format

Share Document