Annie Darwin's death, the evolution of tuberculosis and the need for systems epidemiology

A novel chi-square statistic for detecting group differences between pathways in systems epidemiology

Traditional epidemiology often pays more attention to the identification of a single factor rather than to the pathway that is related to a disease, and therefore, it is difficult to explore the disease mechanism. Systems epidemiology aims to integrate putative lifestyle exposures and biomarkers extracted from multiple omics platforms to offer new insights into the pathway mechanisms that underlie disease at the human population level. One key but inadequately addressed question is how to develop powerful statistics to identify whether one candidate pathway is associated with a disease. Bearing in mind that a pathway difference can result from not only changes in the nodes but also changes in the edges, we propose a novel statistic for detecting group differences between pathways, which in principle, captures the nodes changes and edge changes, as well as simultaneously accounting for the pathway structure simultaneously. The proposed test has been proven to follow the chi-square distribution, and various simulations have shown it has better performance than other existing methods. Integrating genome-wide DNA methylation data, we analyzed one real data set from the Bogalusa cohort study and significantly identified a potential pathway, Smoking → SOCS3 → PIK3R1, which was strongly associated with abdominal obesity. The proposed test was powerful and efficient at identifying pathway differences between two groups, and it can be extended to other disciplines that involve statistical comparisons between pathways. The source code in R is available on our website. Copyright © 2016 John Wiley & Sons, Ltd.

Systems Epidemiology: What's in a Name?

Systems biology is an interdisciplinary effort to integrate molecular, cellular, tissue, organ, and organism levels of function into computational models that facilitate the identification of general principles. Systems medicine adds a disease focus. Systems epidemiology adds yet another level consisting of antecedents that might contribute to the disease process in populations. In etiologic and prevention research, systems-type thinking about multiple levels of causation will allow epidemiologists to identify contributors to disease at multiple levels as well as their interactions. In public health, systems epidemiology will contribute to the improvement of syndromic surveillance methods. We encourage the creation of computational simulation models that integrate information about disease etiology, pathogenetic data, and the expertise of investigators from different disciplines.

HIV infection disrupts the sympatric host-pathogen relationship in human tuberculosis

The phylogeographic population structure of Mycobacterium tuberculosis suggests local adaptation to sympatric human populations. We hypothesized that HIV infection, which induces immunodeficiency, will alter the sympatric relationship between M. tuberculosis and its human host. To test this hypothesis, we performed a nine-year nation-wide molecular-epidemiological study of HIV–infected and HIV–negative patients with tuberculosis (TB) between 2000 and 2008 in Switzerland. We analyzed 518 TB patients of whom 112 (21.6%) were HIV–infected and 233 (45.0%) were born in Europe. We found that among European-born TB patients, recent transmission was more likely to occur in sympatric compared to allopatric host–pathogen combinations (adjusted odds ratio [OR] 7.5, 95% confidence interval [95% CI] 1.21–infinity, p = 0.03). HIV infection was significantly associated with TB caused by an allopatric (as opposed to sympatric) M. tuberculosis lineage (OR 7.0, 95% CI 2.5–19.1, p<0.0001). This association remained when adjusting for frequent travelling, contact with foreigners, age, sex, and country of birth (adjusted OR 5.6, 95% CI 1.5–20.8, p = 0.01). Moreover, it became stronger with greater immunosuppression as defined by CD4 T-cell depletion and was not the result of increased social mixing in HIV–infected patients. Our observation was replicated in a second independent panel of 440 M. tuberculosis strains collected during a population-based study in the Canton of Bern between 1991 and 2011. In summary, these findings support a model for TB in which the stable relationship between the human host and its locally adapted M. tuberculosis is disrupted by HIV infection.

Human tuberculosis (TB) caused by Mycobacterium tuberculosis kills 1.5 million people each year. M. tuberculosis has been affecting humans for millennia, suggesting that different strain lineages may be adapted to specific human populations. The combination of a particular strain lineage and its corresponding patient population can be classified as sympatric (e.g. Euro-American lineage in Europeans) or allopatric (e.g. East-Asian lineage in Europeans). We hypothesized that infection with the human immunodeficiency virus (HIV), which impairs the human immune system, will interfere with this host–pathogen relationship. We performed a nation-wide molecular-epidemiological study of HIV–infected and HIV–negative TB patients between 2000 and 2008 in Switzerland. We found that HIV infection was associated with the less adapted allopatric lineages among patients born in Europe, and this was not explained by social or other patient factors such as increased social mixing in HIV–infected individuals. Strikingly, the association between HIV infection and less adapted M. tuberculosis lineages was stronger in patients with more pronounced immunodeficiency. Our observation was replicated in a second independent panel of M. tuberculosis strains collected during a population-based study in the Canton of Bern. In summary, our study provides evidence that the sympatric host–pathogen relationship in TB is disrupted by HIV infection.

Revealing the complexity of health determinants in resource-poor settings

An epidemiologic systems analysis of diarrhea in children in Pakistan is presented. Application of additive Bayesian network modeling to 2005-2006 data from the Pakistan Social and Living Standards Measurement Survey reveals the complexity of child diarrhea as a disease system. The key distinction between standard analytical approaches, such as multivariable regression, and Bayesian network analyses is that the latter attempt to not only identify statistically associated variables but also, additionally and empirically, separate these into those directly and indirectly dependent upon the outcome variable. Such discrimination is vastly more ambitious but has the potential to reveal far more about key features of complex disease systems. Additive Bayesian network analyses across 41 variables from the Pakistan Social and Living Standards Measurement Survey identified 182 direct dependencies but with only 3 variables: 1) access to a dry pit latrine (protective; odds ratio = 0.67); 2) access to an atypical water source (protective; odds ratio = 0.49); and 3) no formal garbage collection (unprotective; odds ratio = 1.32), supported as directly dependent with the presence of diarrhea. All but 2 of the remaining variables were also, in turn, directly or indirectly dependent upon these 3 key variables. These results are contrasted with the use of a standard approach (multivariable regression).

Whole-genome sequencing and social-network analysis of a tuberculosis outbreak

BACKGROUND

An outbreak of tuberculosis occurred over a 3-year period in a medium-size community in British Columbia, Canada. The results of mycobacterial interspersed repetitive unit-variable-number tandem-repeat (MIRU-VNTR) genotyping suggested the outbreak was clonal. Traditional contact tracing did not identify a source. We used whole-genome sequencing and social-network analysis in an effort to describe the outbreak dynamics at a higher resolution.

METHODS

We sequenced the complete genomes of 32 Mycobacterium tuberculosis outbreak isolates and 4 historical isolates (from the same region but sampled before the outbreak) with matching genotypes, using short-read sequencing. Epidemiologic and genomic data were overlaid on a social network constructed by means of interviews with patients to determine the origins and transmission dynamics of the outbreak.

RESULTS

Whole-genome data revealed two genetically distinct lineages of M. tuberculosis with identical MIRU-VNTR genotypes, suggesting two concomitant outbreaks. Integration of social-network and phylogenetic analyses revealed several transmission events, including those involving "superspreaders." Both lineages descended from a common ancestor and had been detected in the community before the outbreak, suggesting a social, rather than genetic, trigger. Further epidemiologic investigation revealed that the onset of the outbreak coincided with a recorded increase in crack cocaine use in the community.

CONCLUSIONS

Through integration of large-scale bacterial whole-genome sequencing and social-network analysis, we show that a socioenvironmental factor--most likely increased crack cocaine use--triggered the simultaneous expansion of two extant lineages of M. tuberculosis that was sustained by key members of a high-risk social network. Genotyping and contact tracing alone did not capture the true dynamics of the outbreak. (Funded by Genome British Columbia and others.).