Disrupted Human–Pathogen Co-Evolution: A Model for Disease

A major goal in infectious disease research is to identify the human and pathogenic genetic variants that explain differences in microbial pathogenesis. However, neither pathogenic strain nor human genetic variation in isolation has proven adequate to explain the heterogeneity of disease pathology. We suggest that disrupted co-evolution between a pathogen and its human host can explain variation in disease outcomes, and that genome-by-genome interactions should therefore be incorporated into genetic models of disease caused by infectious agents. Genetic epidemiological studies that fail to take both the pathogen and host into account can lead to false and misleading conclusions about disease etiology. We discuss our model in the context of three pathogens, Helicobacter pylori, Mycobacterium tuberculosis and human papillomavirus, and generalize the conditions under which it may be applicable

Disrupted Human–Pathogen Co-Evolution: A Model for Disease

A major goal in infectious disease research is to identify the human and pathogenic genetic variants that explain differences in microbial pathogenesis. However, neither pathogenic strain nor human genetic variation in isolation has proven adequate to explain the heterogeneity of disease pathology. We suggest that disrupted co-evolution between a pathogen and its human host can explain variation in disease outcomes, and that genome-by-genome interactions should therefore be incorporated into genetic models of disease caused by infectious agents. Genetic epidemiological studies that fail to take both the pathogen and host into account can lead to false and misleading conclusions about disease etiology. We discuss our model in the context of three pathogens, Helicobacter pylori, Mycobacterium tuberculosis and human papillomavirus, and generalize the conditions under which it may be applicable

Human and Helicobacter Pylori Coevolution Shapes the Risk of Gastric Disease

Helicobacter pylori is the principal cause of gastric cancer, the second leading cause of cancer mortality worldwide. However, H. pylori prevalence generally does not predict cancer incidence. To determine whether coevolution between host and pathogen influences disease risk, we examined the association between the severity of gastric lesions and patterns of genomic variation in matched human and H. pylori samples. Patients were recruited from two geographically distinct Colombian populations with significantly different incidences of gastric cancer, but virtually identical prevalence of H. pylori infection. All H. pylori isolates contained the genetic signatures of multiple ancestries, with an ancestral African cluster predominating in a low-risk, coastal population and a European cluster in a high-risk, mountain population. The human ancestry of the biopsied individuals also varied with geography, with mostly African ancestry in the coastal region (58%), and mostly Amerindian ancestry in the mountain region (67%). The interaction between the host and pathogen ancestries completely accounted for the difference in the severity of gastric lesions in the two regions of Colombia. In particular, African H. pylori ancestry was relatively benign in humans of African ancestry but was deleterious in individuals with substantial Amerindian ancestry. Thus, coevolution likely modulated disease risk, and the disruption of coevolved human and H. pylori genomes can explain the high incidence of gastric disease in the mountain population

Prevalence, correlates, and prognosis of peripheral artery disease in rural ecuador-rationale, protocol, and phase I results of a population-based survey: an atahualpa project-ancillary study

Background. Little is known on the prevalence of peripheral artery disease (PAD) in developing countries. Study design. Population-based study in Atahualpa. In Phase I, the Edinburgh claudication questionnaire (ECQ) was used for detection of suspected symptomatic PAD; persons with a negative ECQ but a pulse pressure ≥65 mmHg were suspected of asymptomatic PAD. In Phase II, the ankle-brachial index will be used to test reliability of screening instruments and to determine PAD prevalence. In Phase III, participants will be followed up to estimate the relevance of PAD as a predictor of vascular outcomes. Results. During Phase I, 665 Atahualpa residents aged ≥40 years were enrolled (mean age: 59.5 ± 12.6 years, 58% women). A poor cardiovascular health status was noticed in 464 (70%) persons of which 27 (4%) had a stroke and 14 (2%) had ischemic heart disease. Forty-four subjects (7%) had suspected symptomatic PAD and 170 (26%) had suspected asymptomatic PAD. Individuals with suspected PAD were older, more often women, and had a worse cardiovascular profile than those with nonsuspected PAD. Conclusions. Prevalence of suspected PAD in this underserved population is high. Subsequent phases of this study will determine whether prompt detection of PAD is useful to reduce the incidence of catastrophic vascular diseases in the region

Caffeine intake has no effect on sleep quality in community dwellers living in a rural Ecuadorian village (The Atahualpa Project)

More information is needed to better understand the effect of caffeine on sleep quality at the community level. In a population-based, cross-sectional study design, we aimed to assess the effect of caffeine intake on sleep quality by the use of a multivariate exposure-effect model, adjusted for relevant confounders. All Atahualpa residents aged ≥40 years were identified during a door-to-door survey and interviewed with the Pittsburgh Sleep Quality Index (PSQI) and a structured instrument designed to estimate the daily amount of caffeine intake. An exposure-effect model was built using augmented inverse probability weighting taking into account variables that were associated with exposure (using a probit model) and variables that were associated with outcome (in a linear model). Out of 779 eligible individuals, 716 (92%) were included. Consumption of 200 mg/day in 97 (13%). Mean score in the PSQI was 4.5±2.2 points, with 203 (28%) individuals classified as poor sleepers (≥6 points). The exposure-effect model, adjusted for variables associated with the exposure (symptoms of depression, total cholesterol blood levels and smoking) and the outcome (age, symptoms of depression, physical activity and fasting glucose levels), revealed no effect of caffeine intake in sleep quality (average exposure effect: 0.027, 95% C.I.: −0.284 to 0.338, p=0.866). This population-based study shows that caffeine intake has no effect on sleep quality in community-dwelling adults living in a rural village of Ecuador